A14:
SOUNDSCAPE

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0049

The objective soundscape features of the Qianmen Avenue in Beijing
DENG Zhiyong 1 ; LIU Xinran 2 ; CUI Jinwei 3 ; LIU Aili 4,*
1,2,34Music College, Capital Normal University, Beijing, 100048;

College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048.

ABSTRACTWith the continuous development of the sensory experience urban tourism, soundscapeperception has become of importance. Taking the Qianmen Avenue, a typical historical block in theCentral Axis of Beijing Old City as a case study, this paper presents an analysis of the objective
soundscape features just after the first climax of the COVID-19 in January 2021. Based on the modelof "acoustical parameters - classifications - extraction of the keynote and signal and soundmark SPL mapping", the case study shows that although the soundscapes of Qianmen Avenue are complex
and changeable with a LAeq from 55dB to 90dB, the noisy keynotes are combined with the sales’
broadcasting, loudspeakers, tourists and traffic noise; natural sounds are rarely perceived; thewarning bells of passing trams are extremely loud and harsh, causing great disturbance to tourists; noclearly identifiable soundmark has been found; and there is no obvious regularity in the spatial
distribution in the SPL mapping. Apart from the architectural features and recently painted murals,
the soundscape is far away from the “Culture-enriched Beijing” related content that the public
perceives it carries. And due to the COVID-19, an epidemic broadcasting is added as a possible
signal or soundmark.
Keywords: Soundscape, Objective Soundscape Feature, Qianmen Avenue

1. INTRODUCTION

The concept of soundscape was first proposed in 1929 by Finnish geographer Granoe G. Reine ina book of Geographie, with an emphasis on the listener. (1) In the 1960s and 1970s, Canadiancomposer and ecologist R. Murray Schafer and Barry Truax proposed the systematic framework ofsoundscape in the filed of acoustic ecology. In the foundation book of The Soundscape: Our Sonic
Environment and the Tuning of the World, Schafer gave a definition of soundscape that "The sonicenvironment. Technically, any portion of the sonic environment regarded as a field for study. The
term may refer to actual environments, or to abstract constructions such as musical compositions
and tape montages, particularly when considered as an environment,” (2) and Truax clarified
Schafer's definition “to put the emphasis on how that environment is understood by those living
within it - the people who are in fact creating it." (3) Then in 2014, the International Organizationfor Standardization (ISO) defined soundscape as “acoustic environment as perceived or experienced
and/or understood by a person or people, in context." (4)The three basic types of soundscape, andalso the three basic features that sound can be recognized as a soundscape are keynotes (backgroundsounds), signals (foreground sounds as indicators) and soundmarks (native, unique and special soundevents for a community). The original intention of soundscape study is to cultivate people's ability to
dzy@cnu.edu.cn, First Author;
1823785898@qq.com, Presenter;3c651963523@163.com, Co-author;
4,*
beyondtour@163.com, Corresponding Author.
12listen to the surrounding sound environment and then to improve the sound environment.
“Human-sound-context" and their mutual relationship are the three basic layers of soundscape. Afternearly a century of development, soundscape research has developed from its original ecologicalbackground to a more prominent emphasis on the importance of people, society and a specificenvironment. (5, 6) So, in order to provide necessary more complete and effective quantitative datain the field of humanities and social sciences, a case study on the extraction of objective soundscapefeatures is presented in this paper systematically and relatively.

2. METHODOLOGY
2.1 Field Study
2.1.1 Case Study Site
In the context of “Culture-enriched Beijing”, the Qianmen Avenue, a typical historical andcommercial block in the Central Axis of Beijing Old City is chosen for the case study in this paper.
The whole avenue (Figure 1) is from Zhengyangmen Gate in the north to the Zhushikou subway
station in the south, about 1 km long, including Qianmen Avenue, Zhengyangmen Gate, Quanjude
Bell Tower, Dashilan and other main attractions and surrounding full of old commercial streets andalleys (hutong) for residents. This is an extraordinary significant historical area for Beijing. In recent
years, the Central Axis of Beijing, including Qianmen Avenue, has been included in Chinese
Preparatory List of the World Cultural Heritage, which is a kind of protection and restoration of the
Beijing Old City as a cultural center. And soundscape for cultural inheritance is indispensable. The
case study of Qianmen Avenue could provide a more objective data reference for the city soundscaperestoration, the historical cultural protection in a new way, and a valuable mode for the other cities.

Figure 1 – The case study sites of the Qianmen Avenue

2.1.2 Location Recording and Soundwalk
According to the complex situation on site and the local regulatory policy, it is impossible to use
visible or large acoustic measuring equipment like large microphones or sound meters. Therefore, forthe recordings, all the binaural samples were recorded by an Apple iPhone X with a pair ofSennheiser AMBEO smart headsets (calibrated with a standard sound level meter in advance)
plugged into the ears of the recording engineers. Additionally, especially for the soundwalk
recordings, soft soles were required to avoid the sounds of footsteps. In order to simulate a listening
situation of common people, recording engineers could freely or naturally do some slight body
movements such as turning the head and squatting during the soundwalks. 13 typical soundscapesamples used for the analysis are shown in Table 2, including 5 soundwalks (No.1~5) and 8 location
recordings (No.6~13) collected just after the first climax of the COVID-19 in January 2021.
2.2 Objective Features
In terms of the extraction and description of soundscape objective features, the soundscapeobjective feature description model based on "acoustical parameters - classifications - extraction of
the keynote and signal and soundmark - SPL mapping" will be proposed as followed. (7)2.2.1 Acoustical Parameters
1. LAeq
In this paper, we use the equivalent sound pressure level (LAeq) in dBA to represent the soundlevel of each sample as Equation (1):

 1 n 0 .1 L 
LAeq  10 lg  10 pi 
 n i 1
(1)

n - the numbers of equal duration section within a sample in the time domain,
Lpi - the sound pressure level of each duration section.

Then, for the measurement of LAeq, firstly, according to the length of a sample, we cut eachsample into 10 or more sections, calculated the sound pressure level (L p0 ) in dBA with themicrophone’s sensitivity and the equivalent voltage level just at the first section of sample; then the
L pi can be calculated as Equation (2):

L pi  L p0  Lui(2)

Lp0 - the sound pressure level just at the beginning of each sample,
ΔLui - the difference between the equivalent voltage level of each duration section and that of the beginning of this sample,
the equivalent voltage level can be directly read from the recording software.

2. Amplitude envelope
The amplitude envelope shows the shape and contour of the signal amplitude over time. We use
the root of mean square (RMS) envelope closer to the sensitivity of the human auditory system as
Equation (3):RMS 

1 L 1 2
 x [ n]L n 0(3)

L - temporal window width;n - sample number.

3. Dynamic
Dynamic is an important parameter to evaluate the transient characteristics of the sound signal.
The maximum dynamic refers to the sound pressure level (SPL) difference between the maximum
useful signal and its background noise, while the efficient dynamic used in this paper refers to the
SPL difference between the maximum useful signal and the minimum one, which can also be
expressed by the level difference of the signal.

4. Frequency spectrum
Frequency spectrum refers to the spectrum diagram where the amplitude of each frequency
component contained in the signal changes with the frequency, which can intuitively reflect which
frequencies the signal is composed of. The time domain discrete signal x[n] can be transformed to
the frequency domain discrete signal X[θ] through the Fourier transformation as Equation (4). A
frequency spectrum in one-third octave is used in this paper.
n  

X ( )   x[n]e  jn(4)n  

e  jθn  cos θn  j sin θnθ - digital frequency in rad/sample
n - sample number.
2.2.2 Classifications
The second type of objective soundscape features is content classification. Based the basic types
of keynote, signal and soundmark, six categories of sound objects proposed by Schafer (2) is used inthis paper as Table 1.
Table 1 – Six categories of sound objects for soundscapeCategories

Code

Natural Sounds
Human Sounds
Sounds And Society
Mechanical Sounds
Quiet And SilenceSounds As Indicators

NS
HS
SAS
MS
QAS
SAI

2.2.3 Extraction of The Keynote and Signal and Soundmark
Three main soundscape types and features of Keynote, Signal, and Soundmark (3) are extracted
by means of expertise to analyze the sound environment and its meaning in detail.
1. Keynote
Against other sounds, keynote sounds are those heard by a particular lineage frequently enough toform a natural or social sound environmental background , such as the sound of wind in a valley
village, or the sound of traffics in a big city. Most but not all of the keynote sounds are lower in thesound pressure level, stable in the frequency spectrum, and then often ignored by the people.
2. Signal
The signal sounds are those which directly give an attention or a particular information, such as
traffic alert and church bell sounds, that may often be organized into some codes to transmit a
particular message quickly and clearly. They are foreground sounds as indicators. But for now,
signals are also widely to refer to some special sound event or sound object in the foreground.
3. Soundmark
The term of “soundmark” is derived from “landmark” to refer to a special sound event which isnative, unique and specially regarded or noticed by the people in a community, a village, a city, acountry or etc. Once a soundmark has been identified, its nativity, sociality and history related to its
native time-spatial identifiability (4), deserves to be protected.
2.2.4 SPL Mapping
In this paper, a SPL mapping (in LAeq of different colored blocks obtained from soundwalks andlocation recordings) with the marks of the important sound events (or sound objects) as Figure 3 isused to represent the SPL spatial distribution of sound events in different regions.

3. RESULTS
3.1 Acoustical Parameters
The performance of the four acoustical parameters, including the LAeq, amplitude envelope,
dynamic and frequency spectrum, are shown as Figure 2.

(a) LAeqs and dynamics for soundwalks(b) Amplitude envelops for soundwalks(c) LAeqs and dynamics for location recordings(d) Amplitude envelops for location recordings(e) Frequency spectrums
Figure 2 – Acoustical parameters of the Qianmen Avenue
According to the LAeq, amplitude envelope and dynamic, the acoustic time domaincharacteristics of Qianmen Avenue are:
 The dynamic and amplitude envelope fluctuate greatly, and more in signal sounds.
 The amplitude envelope fluctuates significantly in commercial streets (No. 2, 3, 4, 8).
 The LAeq is about 55 to 90dBA.

 The keynote sound may not be consistent.
 The soundmark of tram ringtone (No.7) is obviously different from others.
According to the frequency spectrum, the energy of the sound frequency is relatively affected byhuman voice and commercial noise; the medium frequency bandwidth is wide, and the soundmark oftram ringtone (No.7) is mainly in some high frequencies.
3.2 Classifications
Six categories and the three soundscape types of Qianmen Avenue is shown in Table 2.
Table 2 – Six categories and the three soundscape types of Qianmen Avenue
Six categories of sound objects
Categories

Code

Numbers

Percentage*

Natural Sounds
Human Sounds
Sounds And Society
Mechanical Sounds
Quiet And SilenceSounds As Indicators

NS
HS
SAS
MS
QAS
SAI

13 (All)
13 (All)
13 (All)
13 (All)
1 (No.5)
5 (No.5, 7, 10, 11, 12, 13)

100%
100%
100%
100%
7.69%
38.46%The three soundscape types
KeynoteSignalKS

13 (All)
6 (No.5, 7, 10, 11, 12, 13)100%
46.15%

SoundmarkM

1 (No.7 possible)

7.69%*Each category or type of sample is repeated statistics, that when the content in a sample contains multiple categories
or types, the numbers of samples are counted simultaneously.

Based on the contents, the Qianmen Avenue are riched of different sounds, including naturalsounds, human voices, social sounds and mechanical sounds. The natural sound is mainly wind; themechanical sound is relatively extensive, resulting in a wide range of dynamic; the "quiet and silent"
only appears in the soundwalk of No.5 in Wangpi Alley, and it is very quiet when there is noconstruction sound appearing. The proportion of “sound as indicators (SAI)” sound closes to 40%.
From the perspective of the three soundscape types, keynote sounds are clearly recognizable, andconsist essentially of human voices, social sounds and a very small amount of natural sounds. The
most obvious signals are the tram ringtones and car sirens, bells and epidemic prevention
broadcasting for COVID-19. The tram bell ringtones can be recognized as a soundmark deliberately
restored in history, but in the current, a type of “sound as indicators (SAI)” or “signal sound”
function is more obvious. The unique sales’ cries on the street in the historical period may be used as
the expected soundmark, but under the influence of commercialization and social or historicalchanges, it becomes difficult to be detected and identified or even almost disappear. It is difficult toclearly extract such soundmark from the soundscape samples collected on the area. Instead of natural
peddling appears in the shop broadcasting and peddling performance, due to its too high volume andtoo poor sound quality, it may be difficult for the audience to become soundmarks, or even mixed
with human voices and social sounds into a noisy keynote.
3.3 Keynote, Signal and Soundmark
The keynotes, signals and soundmarks and their analysis are listed in Table 2.

Table 2 – Keynotes, signals and soundmarks of Qianmen Avenue
SampleNo.

Location

LAeq

Duration

(dBA)

mm:ss

Description and analysis
Soundwalks

The south end of

11:56

Many sounds are composed of tourist voice, sales’ broadcasting andtram bell ringtones. There are traffic noise at both the south and northends due to the nearing traffic main roads. Except for the wind, lessnatural sound could be perceived. SPL is moderate and stable, with thehighest one of 85dBA in a short period.
Keynotes: Tourists, Sales’ broadcasting, Some traffic noise;
Signals and soundmarks: None.

10:00

Langfangtoutiao Alley and Langfangertiao Alley are two narrowcommercial streets with bicycle driving and some construction in
working. The wind is bigger than other places. The constant SPL isaround 75dBA in a longer period.
Keynotes: Wind, Sales’ broadcasting, Construction, Tourists;
Signals and soundmarks: None.

10:33

Dashilan is an important commercial street with numerous shops and alarge flow of people, while Menkuang Alley is a resident place withfew of stores or tourists. The difference of SPL is about 5dBA for these
two streets in different functions.
Keynotes: Wind, Sales’ broadcasting, Tourists;
Signals and soundmarks: None.

10:30

Xianyukou Alley is a famous food street with lots of tourists for eating.
The east end is near a traffic road with an average SPL of 81dBA,
while the SPL of resident Dajiang Alley is low.
Keynotes: Sales’ broadcasting, Tourists;
Signals and soundmarks: None.

05:28

Wangpi Alley and most of Alleys at the south part of Qianmen Avenue
are the relatively quiet residential areas. There are no shops in theseareas. Some local social sounds and lite natural sounds such as windand birds song can be heard.
Keynotes: Natural sounds, Local social sounds;
Signals and soundmarks: None.

Qianmen Avenue
1

tothe north end on

73

Zhengyangmen
Gate
Langfangtoutiao
2

Alley
to

76

Langfangertiao Alley

Dashilan3

to78

Menkuang Alley

Xianyukou Alley
4

to

81

Dajiang Alley
Wangpi Alleyand5most of Alleys at the58

south part of
Qianmen Avenue

Location recordings

6

Zhengyangmen

73

01:00

Zhengyangmen surrounded by traffic roads is an ancient main buildingin the north end of Qianmen Avenue and currently under maintenance.
The sound in this area is mostly traffic noise and relatively stable at
65dBA
Keynotes: Traffic noise;
Signals and soundmarks: None.

00:48The Zhengyangmen Station of the sightseeing tram is just between
Zhengyangmen and Qianmen Arch. The tram bell ringtones soundunique but very sharp and loud around 82dBA and up to 90dBA with ashort period.
Keynotes: Traffic noise, wind;
Signals and soundmarks: Tram bell ringtones.

71

01:06

The East Entrance of Dashilan is at the cross of the commercial street
and Qianmen Avenue. The SPL is stable around 61dBA. Some
traditional sales’ cries could be heard not clearly.
Keynotes: Tourists noise, business noise;
Signals and soundmarks: None.

70

00:450

The West Entrance of Dashilan is nest to a noisy traffic road.
Keynotes: Traffic noise, tourists noise, business noise;
Signals and soundmarks: None.

00:55

The soundscapes in the north section of Qianmen Avenue are rich ofvarious interesting contents of No. 2, 3, 4, 6, 7, 8 and No. 9. The tram
bell ringtones could be heard sometimes.
Keynotes: Tourist sounds, business sounds;
Signals: Tram bell ringtones;
Soundmarks: None.

01:37

A Hammer candy store is located in the middle section of Qianmen
Avenue. A folk song related some traditional sales’ cries is amplified
in loop. It is hard to say this is a soundmark without a native
identification of the time and the location.
Keynotes: Tourist voice, business sounds;
Signals: Music broadcasting;Soundmarks: None.

00:35

Jinwu Shopping Mall is the only shopping mall in Qianmen Street.
There is a epidemic broadcasting for COVID-19 in a soft level at themain entrance.
Keynotes: Broadcasting sounds, business sounds;
Signal sound: Epidemic broadcasting for COVID-19;
Soundmarks: None.

00:30

The south entrance near to the Zhushikou subway station is theintersection of a main traffic road with a large of traffic flow, while thesouth section of Qianmen Avenue is mainly the resident area in a
relatively quiet sound environment.
Keynotes: Traffic noise;
Signal sound: Car sirens;
Soundmarks: None.

Zhengyangmen
7

Station of the85sightseeing tram

8

9

10

11

12

13

The East Entrance ofDashilan

The West Entrance ofDashilan

The North Section of
Qianmen Avenue

The Hammer CandyStore

Jinwu Shopping Mall

The south entrance of
Qianmen Avenue

71

77

75

88

3.4 SPL Mapping
Figure 3 shows a SPL mapping of Qianmen Avenue. Most of the SPLs are above 70dBA, which
varies between 55 and 90dBA, the SPLs in the north section are higher than those in the south, andthe SPL of the pedestrian street in the middle is smaller than those of Dashilan Commercial Street
and Xianyukou Food Street. So many sales’ broadcasting by loudspeakers and a large number oftourists lead to an extremely noisy sound environment, while the natural sound is rarely perceived.
The unique soundmark of tram bell ringtone is interesting but disturbing because of their SPL up to
90dBA sometimes. The traffic noise at intersection has a great impact. Therefore, the soundwalks
and location recordings in this paper are sampled in the winter just after the epidemic COVID-19 of
the year of 2020, then the people flow and the SPLs are lower than expected.

Figure 3 – SPL Mapping of Qianmen Avenue

4. Summary

The soundscapes of Qianmen Avenue are relatively complex with various amplitude envelopes,
dynamics and frequency spectrums. The SPLs are between 55 and 90dBA with a transient peak of up
to 95dBA. The SPLs in the north section are higher than those in the south, and the SPL of thepedestrian street in the middle is smaller than those of Dashilan Commercial Street and Xianyukou
Food Street. Some signal sounds are often appeared, while the type of "quiet and silence" appeared
rarely. Due to mixing of the business noise, sales’ broadcasting,tourist noise and traffic noise, thekeynote sounds may not be too low, while the natural sounds and some potential soundmarks are
rarely to be heard and identified. In some past historical periods, the sales’ cries in human voice are
popular as unique and culture-enriched soundmarks. But nowadays, replaced by the broadcasting inloudspeakers. The restoration of the traditional sightseeing tram give a new but tradition likesoundmark of bell ringtone. But the sound is too loud and harsh, the sound quality is too poor tocause a great interference to the local people and tourists. Apart from the architectural features andrecently painted murals, the soundscape is far away from the “Culture-enriched Beijing” related
content that the public perceives it carries. And due to the COVID-19, an epidemic broadcasting is
added as an unique sound event or a possible signal or soundmark in the Qianmen Avenue.
For soundscape, the RMS amplitude envelope is a macroscopic detection to obtain some variation
information, while the dynamic takes that of a whole. When the sound has a high dynamic, a drasticchange or a prominent sound object or sound event in the time domain could be explored. Therefore,
combining the dynamic and the amplitude envelope would be more conducive to the extraction of thecontent of soundscape. Furthermore, some frequency spectrum often represents an unique timbre
information, and leads us to explore a possible soundmark.
With the intensification of the urbanization process in the modern society, the content of theurban soundscape has become more and more complex, and the research scope of the soundscape has
become more and more extensive. However, back to the original concept and methodology ofsoundscape, it is very meaningful to reorganize and study the objective features and their extraction
methods of soundscape for the long-term urban soundscape research and soundscape composition or
new music creation.

ACKNOWLEDGEMENTS

This study was supported by grants from the National Natural Science Foundation of China (toLIU Aili, No.41871130) . And many thanks to the recording engineers, BAI Danfeng, TONG
Kaicheng, DONG Kexin.

REFERENCES
1.
2.
3.
4.
5.
6.
7.

J. D. Porteous, J. F. Mastin. Soundscape. Journal of Architectural Planning & Research, Vol.2, No.3,
1985, pp.169-186.
Schafer R M. The Soundscape: Our Sonic Environment and the Tuning of the World. New York:
Alfred Knopf, 1997. p. 5-16,133-139,259,261.
Truax, Barry. Acoustic Communication. Greenwood Press, Westport, CT: Ablex Publishing, 2001.11.
International Organization for Standardization Acoustics-Soundscape-Part 1: Definition andConceptual Framework. ISO 12913-1:2014(E). Switzerland 2014.
DENG Zhiyong, LIU Aili, ZHOU Guowen, HUA Wei. Lineage, Settlement, Sound: from a Cultural
View of Soundscape (Chinese Version). Beijing: Tourism Education Press, 2015, 10.
KANG Jian. Urban Sound Environment. Taylor & Francis, 2006, 10.
BAI Danfeng. The objective soundscape features and their extractions: a case study on the current
situation of the Central Axis of Beijing Old City. Thesis of Master of Fine Arts, Capital Normal
University, Beijing, 2021, 6.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0056

Audiovisual evaluation of informal learning spaces in primaryschool using eye tracking and physiological measurements
Fei QU1; Qi XIE2; Peichen Xu3
1, 2

School of Architecture and Urban Planning, Shenzhen University, China
3

School of Architecture, Harbin Institute of Technology, China

ABSTRACTWith the advent of the digital and smart era, the campus buildings of the future will create interactive, flexible,
and borderless informal learning spaces. Focused on the informal learning at primary schools, the study
explored school-age children's multisensory cognitive experiences and learning benefits in the informal
learning space using a combination of physiological feedback experiments and subjective evaluations,quantified and compared children's attention levels, cognitive load and emotional disposition in different
spatial scenarios. The paper highlights the positive effects of audiovisual interaction in informal learningspace on establishing children’s concentration and effective cognitive learning. It finally proposed a spatial
strategy that aims to enhance the embodied learning experience in future primary schools.
Keywords: Psycho-physiological, Eye-tracking, Informal learning, Embodied cognition,

1. INTRODUCTION
With new technological tools applied with education, the learning space environment onelementary school campuses is changing. Interactive, flexible, and borderless informal learning spaces
allow for learning behaviors that are no longer limited to the traditional classroom and can occur
anywhere and anytime in spaces such as atriums, overheads, and hallways(1). The emergence of
informal learning spaces will also change the way school-age children perceive and interact in space.
The body is the most instinctive way for children to recognize and understand their environment.
Children's knowledge acquisition in the informal learning space is often accompanied by embodied
cognition and the involvement of multi-sensory systems (2). In the theoretical framework of embodied
cognition, the body, cognition, and the environment are integrated, while the body creates immersive
learning experiences through multisensory triggers such as observing, touching, listening, and talking(3). Recent studies have used eye-tracking techniques to analyze the difficulty and attentionaltendencies of human beings in receiving and processing spatial information, and these techniques havebroken through previous methods of exploring spatial cognition only through subjective evaluations
such as questionnaires (4,5). Meanwhile, more studies have used wearable devices and physiologicalhuman factors analysis to monitor emotional fluctuations and changes in psycho-physiological statesof the users (6).
This study will combine eye-tracking and physiological monitoring methods to measure and collectphysiological feedback data from children in audiovisual interactive informal leaning spaces. It willcompare the effects of different informal learning scenes on embodied cognition, which is expected
to guide the creation of future campus informal learning spaces from children's perspective.

2. METHODS
2.1 Experimental Design
The study used the semi-outdoor corridor of Hongling Experimental Primary School in Shenzhen
1

fqu@szu.edu.cn
2070325005@email.szu.edu.cn
3
20S034052@stu.hit.edu.cn

2

as a prototype space. Six experimental scenarios were constructed and generated, using four factors
as control conditions: wall openness, physical interaction facilities, electronic learning facilities, andsoundscape creation (Figure 1). Previous research has shown that conversations in space have anegative impact on children's cognitive learning benefits. The experiment used a 1-minute corridor
conversation and a 1-minute narration about educational knowledge collected on site as auditory
stimulus samples superimposed in Scene 4, generating two audiovisual immersive learning spaces in
Scenes 5 and 6, respectively.

Figure 1 – Experimental scenes and process
2.2 Experimental Procedure
Twenty-four primary school students aged 6-12 with normal hearing and vision were invited toparticipate in the experiment, which was conducted in a multifunctional space with constanttemperature and humidity and good sound insulation. Participants were asked to be seated quietly for
15 minutes before the official start of the experiment and to listen to the researcher's explanation of
the entire experiment. After obtaining informed consent from the parents, the investigators put on the
ErgoLAB smart physiological bracelet, Tobii Pro Glasses3 wearable eye-tracking device andheadphones to correct the physiological and eye-tracking signals. Visual scene images were shown on
a 70-inch electronic display. Previous studies have shown that still images with sound material canstimulate subjects to produce original field experiences (7).
Physiological data were monitored during the experiment using ErgoLAB3 software for eachexperimental segment, and the experimental procedure is shown in Figure 2. First, the data of 2minute resting state was used as the baseline index, reflecting the relaxation state of the subjects
before the stimulation. Then each experimental scene was played for 1 minute in turn, and the subjects'
eye movements and physiological data were monitored at the same time. The corresponding
questionnaires were filled in during the playback interval of audio-visual scenes. The total durationof the experiment was about 15 minutes.
2.3 Eye Movements and Physiological Indicators
The metrics collected in this experiment are shown in Table 1. The eye-movement metrics collectedin this experiment include those related to specific areas of interest (AOI), which represent the timeand efficiency of subjects' processing of visual information in the area of interest; and non-AOI related
eye-movement and pupil data, which can indicate the ease of cognitive processes. Physiologicalevaluation metrics include skin electroactivity (EDA) and heart rate variability (HRV), which are
expressed as Skin Conductance Level (SCL) and standard deviation of NN interval (SDNN),

respectively.
Table 1 – Interpretation of eye movement and physiological feedback indicators
Name of indicator (unit)

Interpretation

Time to First Fixation (TTFF)

Time from the start of the time interval to the appearance of thefirst gaze point at the target AOI (s).

First Fixation Duration (FFD)

Duration from first gaze at the target AOI area to line of sight
departure (s).

Total Fixation Duration Ratio

Total gaze time in a given AOI area as a percentage of all gaze

(TFDR)

time (%).

Number of Visits (NV)

Total number of visits within the AOI.

Average Pupil Diameter (APD)

Average pupil size diameter during viewing (mm).

Average Number of Blinks (ANB)

Average number of blinks per second during viewing.

Average Number of Saccade

Average number of saccades per second during viewing.

(ANS)
Skin Conductance Level (SCL)

Changes in skin conductivity caused by skin vasoconstriction andsweat gland secretion (µS).

Standard Deviation of NN

Standard deviation of interbeat that explains the differences inInterval (SDNN)

cardiac rhythm activity.

2.4 Subjective Evaluation
After the audiovisual experience in scenes 5 and 6, the researcher invited the participants to makea subjective evaluation of the level of impact of the sound and the level of understanding of the content
of the exhibit, using a 10-point Likert scale. The subjective ratings will be used to correlate withphysiological and eye-movement behavioral indicators to help understand the cognitive and learningbenefits of the different scenes.

3. RESULTS
3.1 Changes in Gaze Behavior Between Different Scenarios
Figure 2 shows the perceptual behavior of the subject children in different scenarios. When theinterface was changed and facilities were gradually added in the scenes, children's attention was
attracted to the novelty in various degrees. Subjects' visual attention was maximized when an electric
explanatory human voice matching the teaching facilities on the wall was superimposed in Scene 4(Scene 6).
Therefore, compared with the increase of interface openness and physical interaction facilities in
the pan-learning space, the subject children are most sensitive to the insight of teaching-type
interactive facility exhibits, which combined with the superimposition of sound scenes will
significantly increase the gaze rate and interest of the scenes.
Table 2 focuses on the effect of instructional facilities in the scene on transient visibility, attention,and ease of cognitive understanding, using a one-way ANOVA to show significant changes in the four
AOI-related eye movement data with respect to the scene. The presence of the interactive panels
attracted the immediate attention of the subject children as seen from the Time to First Fixation, but
the presence of the conversation noise interfered with the children's first attention and reduced theattention to the panels. The immersive scene with the superimposed interactive explanatory sound reenhanced the instantaneous visibility of the display board. The duration of first fixation and thepercentage of total fixation time in Scene 6 were the highest, indicating that children's potential
concentration and interest reached the maximum in the audiovisual immersion scene. From the number

of visits, it is clear that the subjects' sight fell on the wall more scattered and switches frequently whenviewing purposelessly in the control scene, and the number of visits decreased and the length of visits
increased when there was an intelligent display board, indicating that the subjects were more focusedon processing visual information. The presence of conversational sounds increased the number ofvisits, indicating that subjects underwent many sight switching behaviors between inside and outsideof the exhibit, and frequent repeated visits implied that cognitive difficulties were encountered, which
may demonstrate that conversational sounds significantly interfered with the cognitive comprehensionof the exhibit. When the background sound was eventually switched to an interactive explanation
related to the content of the exhibit, the subject children showed significantly lower sight switching
and longer fixation time, indicating that both attention and comprehensibility were optimal at thispoint. The interaction of the audiovisual scenes provided an effective improvement in the children's
potential attention.

Figure 2 – Distribution of gaze points
Table 2 – One-way ANOVA of the mean of each eye movement index
Time to First
Fixation (s)First Fixation Duration(s)

Total Fixation
Duration Ratio (%)

Number of Visits
(n)

3
4

1.727
.000

.207
.173

12.83
79.53

12.00
10.67

5

.187

.160

62.08

11.33

6

.000

.687

97.61

3.67

F
Sig.

19.851＜.001***

8.537＜.001***

142.870＜.001***

16.550＜.001***

Scenes***p＜.001.

3.2 Physical-emotional Changes between Scenes
The effects of different strategies on children's cognitive load and psychological disposition havebeen predicted. Table 3 presents a comparison of the means of the physiological indicators between
the two scenarios using an independent sample t-test, showing only the physiological variables that
had a significant effect.
First, increasing the openness of the interface significantly reduced the electrodermal indicators of
the subjects, indicating that the children's tension was relieved, with no significant effect on otherphysical emotional indicators.
Both the addition of interactive play facility and instructional panel significantly elevatedelectrodermal indicators (EDA) in the subjects, suggesting that visual stimuli triggered emotions suchas physical pleasure and arousal (8). Both indicators also elevated heart rate variability (HRV) to someextent, significantly at the p < 0.1 level, indicating that parasympathetic nerves were in a dominantstate. The results predicted that simply adding play and educational facilities reduced cognitive stresswhile evoking happy emotions and engaged children in a relaxed state. The addition of interactive
rides also resulted in a significant decrease in pupil diameter, indicating that children had reduced
visual engagement and cognitive load in this scenario and were more likely to be in a low-stressemotional state. The smart interactive display board on the wall had no significant effect on pupil
diameter, but significantly reduced the number of blinks and eye jumps per second. Since frequent
blinks and eye jumps represent distracted and fast-moving eyes that do not acquire effective visual
information, the presence of the smart display board seemed to reduce aimless gaze point jumps andenhance perceptual information acquisition.
Superimposed corridor conversations increased the number of eye jumps, representing theinterference of noise that inhibited gaze behavior. However, superimposing the electric lecture soundsignificantly increased physio-emotional sensitivity and perceptual learning efficiency. Subjects had
significantly higher electrodermal levels and lower heart rate variability indexes in scenario 6,
indicating that the audiovisual interactive learning scenario awakened children's bodily functions andput sympathetic nerves responsible for processing information in a dominant state, which would likely
enhance effective information processing and learning memory.
Table 3 – Comparison of physiological index before and after the scenario setting strategy
Mean
F
p
t
df
a. Enhanced openness
EDA
Scene 1
.202
5.934
.019*
.446
46
Scene 2
.175b. Add play facility
EDA
Scene 1.202
8.234
.006**-1.49946
Scene 3.293
SDNN
Scene 1
.016
3.120
.084
-1.44846
Scene 3.308
APD
Scene 1
4.067
9,737
.003**
1.065
46
Scene 33.982
c. Add educational panel
EDA
Scene 3.293
22.194＜.001***-3.62346
Scene 4.630
SDNN
Scene 3
.308
3.592
.064
-.397
46
Scene 4.386
ANB
Scene 3.320
20.046＜.001***.358
46
Scene 4.297
ANS
Scene 35.018
7.435
.009**
2.234
46
Scene 44.305
d. Add conversation noise
ANB
Scene 4.297
27.197.000.471
46
Scene 5.267
ANS
Scene 4
4.305
4.841
.033
-1.201
46
Scene 54.713
e. Add explanatory human voice
EDA
Scene 4.630
21.927＜.001***-1.51346
Scene 6
.830

SDNN
APD
ANS

Scene 4
Scene 6

.386
-.243

28.594＜.001***

4.393

46

Scene 4
Scene 6

3.833
3.955

5.312

.026*

-1.17446

Scene 4
Scene 6

4.305
4.092

18.449＜.001***.587

46*p＜.05, **p＜.01, ***p＜.001.

3.3 Relationship between Physiological Indicators and Subjective Understanding
After superimposing the two scene stimuli with sound, the subject children provided a subjective
rating of scene perception on a scale of 1-10 in the questionnaire. A t-test on the mean of the subjectivescores revealed that the subject children perceived the explanatory voice in Scene 6 to be more
conducive to understanding the interactive instructional message (t=0.184, p=0.034). The subjects
understood the interactive instructional message in Scene 6 to a greater extent, however this change
was not statistically significant (t=5.992, p=0.237).
Subjective knowledge acquisition of the subjects was significantly positively correlated with thepercentage of fixation time in the AOI (r=0.799, p<0.001), significantly negatively correlated with
first fixation time (r=-0.462, p=0.001), and negatively correlated with the number of blinks (r=-0.349,
p=0.015) and overall number of visits (r=-0.803, p<0.001). The results suggest that the shorter thetime children spent attending to the interactive learning device for the first time, the longer theylooked at it, and the lower the blink and eye switch rates, the better their subjective knowledge
acquisition.
Also, the degree of knowledge acquisition was significantly and negatively correlated with heart
rate variability SDNN (r=-0.587, p<0.001), suggesting that better knowledge information processing
is accompanied by a decrease in heart rate variability, when sympathetic nerves, which are responsible
for processing information, dominate and are in a state of cognitive load.

4. CONCLUSIONS
Based on embodied cognitive theory, the study measured primary school children's visual
behavioral responses and physiological feedback to different informal learning scenes through virtual
audiovisual perception experiments, and used quantitative comparisons to infer the visibility,
comprehensibility, interest and emotional arousal of different stimulus elements for children. The
article first defines that primary school children are more likely to awaken first attention and visualinterest in audiovisual interactive learning scenes, where higher emotional arousal and effective
cognitive engagement are also found. The supply of open interfaces and physical interaction facilities
can appropriately reduce children's cognitive load and emotional stress in informal learning scenes.
The paper suggests that the integration of soundscapes should be emphasized in the design of future
informal learning spaces, which provide empirical data reference for campus design and renewal fromthe embodied perspective of school-age children.

ACKNOWLEDGEMENTS
The study was supported by Shenzhen Science and Technology Program (Project No.
ZDSYS20210623101534001).

REFERENCES
1. Mei Q. The Application of Informal Learning Space in the Design of Contemporary Primary Schools in
Italy. Urbanism and Architecture, 2019.
2. Wilson M.Six views of embodied cognition. Psychonomic Bulletin & Review, 2002; 9(4):625–636.
3. Ale M, Sturdee M, Rubegni E. A systematic survey on embodied cognition: 11 years of research in child–computer interaction. International Journal of Child-Computer Interaction, 2022; 33:100478.
4. Deng L, Luo H, Ma J, et al. Effects of integration between visual stimuli and auditory stimuli on
restorative potential and aesthetic preference in urban green spaces. Urban Forestry & Urban Greening,2020; 53: 126702.
5. Liu F, Kang J. Relationship between street scale and subjective assessment of audio-visual environment
comfort based on 3D virtual reality and dual-channel acoustic tests. Building and Environment, 2018;
129: 35-45.
6. Li Z, Kang J. Sensitivity analysis of changes in human physiological indicators observed in soundscapes.
Landscape and Urban Planning, 2019; 190: 103593.
7. Liu Y, Hu M, Zhao B. Audio-visual interactive evaluation of the forest landscape based on eye-tracking
experiments. Urban Forestry & Urban Greening, 2019; 46: 126476.
8. Suko Y, Saito K, Takayama N, et al. Effect of Faint Road Traffic Noise Mixed in Birdsong on thePerceived Restorativeness and Listeners’ Physiological Response: An Exploratory Study. International
Journal of Environmental Research and Public Health, 2019; 16(24): 4985.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0180

The effect of the sound experience process on the soundscapenaturalness in urban parks
Chutian Sun1; Qi Meng2; Da Yang3
1

Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,
Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology,
Harbin, China

2

Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,
Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology,
Harbin, China

3

Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,
Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology,
Harbin, China

ABSTRACTIn previous soundscape studies, the soundscape naturalness has been an important metric to evaluate thequality of soundscapes in urban parks. However, the effect of the sound experience process on thesoundscape's naturalness has not received much attention. Thus, this study investigates the effect of the soundexperience process on the assessment of soundscape naturalness in a typical urban park through a soundwalk
survey. The results are as follows: first, different sound experience processes affect the assessment ofsoundscape naturalness in the urban park. Second, the results of the soundscape assessment in the soundexperience processes conform to the peak-end rule. At the end of the soundscape process, the peak assessmentof the soundscape naturalness appears at the end of the sound experience process, which significantly affects
the overall assessment. Third, comparing different processes of sound source change experiences, it is foundthat the soundscape naturalness of the artificial-natural sound experience process is higher than that of thenatural-artificial sound experience process (p < 0.05). Consequently, the design of natural sounds in the pathof urban parks is expected to intervene in visitors' soundscape naturalness experience.
Keywords: Sound experience process, Soundscape naturalness, Urban parks

1. INTRODUCTION
In the context of urbanization，urban environment pose challenges for human health and wellbeing.
There is growing evidence that natural environments can benefit human health (1), such as reduced
stress, improved mental health, and reduced depression etc. In previous studies of the naturalenvironment, much of which focused on cognitive benefits(2). Herzog et al. have proved that originalnatural environments facilitating attention restoration than sports recreational environment (3).
Tennessen and Cimprich showed that natural views were associated with better performance on
attentional measures (4). Fisher et al. showed the perceived naturalness contribute positively to theperceived restorativeness of a site (5). Subsequently, naturalness is often regarded as a valued factorof environment (6). The above studies focus more on the assessment of the naturalness of the visualenvironment rather than the sound environment. Moreover, due to people's visiting habits in urban
public open spaces, they are more making an assessment of the naturalness in the experience process.
t84288686@126.com

1
2
3

mengq@hit.edu.cn
da.yang@hit.edu.cn

Nonetheless, the effect of sound experience process on soundscape naturalness has given insufficientconsideration
Peak-end rule can effectively explains the characteristics of the experiential process. According toFredrickson and Kahneman, the peak-end rule was a experience process, in which the assessment ofexperiences based on their feeling at the most and the final point (7). Some researches had proposedthat the most extreme (Peak) and final (End) moment of an individual ’s experience during an event
might be determined the quality of the experience. The peak-end rule for experiences had been
conducted in several studies (8,9). Baumgartner et al. reported that television commercials were more
popular with viewers, if the commercials had high peaks of point and positive endings (10). Diener et
al. also suggested that the respondents enjoyed a wonderful life that ended suddenly as being better
than one with mildly pleasant years (11). Chow et al. found that the effects of weekends could boost
an individual’s positive emotions, such as contentment, love, and joy (12). These studies indicatedthat there exist the highest and end moments that tend to affect the individuals ’ experience. However,for the soundscape naturalness, how the peak-end rule works on the sound experience processes inurban parks is as yet unclear.
Generally, environmental naturalness studies had usually focused on the effect of environmental
naturalness in sampled sites. In terms of the naturalness assessment of the visual environment, someresearchers had suggested that it is more reliable to assess participants' views of the naturaln ess on
assessments of photos in sample sites of nature landscape (13, 14, 15). Ibarra et al. investigated theways different image feature types perceived naturalness in conjunction, and show the water and non veiling vegetation were positive predictors of perceived natu ralness (16). Similarly, along with visualenvironment perception, the perceived naturalness of the acoustic environment equally affect ed thequality of environmental naturalness perception. In terms of naturalness assessment of the acoustic
environment, Li et al. had investigated the soundscape naturalness of the sample sites in mountainous
urban parks (17). The above study found that the aggregation of the assessment results of sample -sites
as the whole results. However, the effect of the process from artificial to natural sound dominance on
the soundscape naturalness has not been explored.
Therefore, this study aimed to explore the following research questions: 1. In urban parks, will
different sound experience process affect the soundscape naturalness assessment? 2. Whether thesoundscape naturalness assessment is consistent with the peak -end rule in the sound experience
processes? 3. How do sound source process affect the soundscape naturalness assessment in the urban
park? This study conducted a soundwalk survey to investigate the effects of sound experience process
on soundscape naturalness in a typical urban park in Harbin, China, as a case study.

2. METHODOLOGY
2.1 Site survey
A typical urban park named Zhaolin Park in Harbin, China, was chosen as the site for the study. It
is divided into several areas according to the paths. The reasons for selecting th e park are as follows.
First, it offers a variety of paths for visitors, providing a spatial contextual basis and various options
for different sound experience process. Second, the park contains different types of sounds. It contains
spaces both natural or artificial sounds as the dominant sounds, which facilitates research and analysis
of soundscape naturalness. Third, the park contains different types of acoustics environments, such as
quiet or noisy space, which facilitates control of large changes in S PL.

Figure 1 Master plan of the park, the paths of the soundwalk and the procedure for soundwalkThe site survey included two parts: a sound naturalness survey and SPL survey. In terms of thesoundscape naturalness survey, it was based on the noticed sounds in each locations. By assessing thedominant sounds of the acoustic environment, it can determine whether the different locations are
dominated by artificial sounds or by natural sounds. On this basis, each location is defined as"artificial" or "natural" in the urban park. The location 14, 15 and 17 are defined as "natural", theother locations are defined as "artificial".
In terms of the SPL survey, the SPL in the park was assessed using equivalent continuous A weighted sound pressure levels, measured by a BSWA 801. The distance between the measurement
location and a wall or other major reflective surface was at least 1 m, and the distance between themeasurement location and the ground was 1.2-1.5 m (18). The SPL was read, and instantaneous data
were obtained every second for 5 minutes in each measurement location. As shown in Figure 1,
location 8 (LAeq, 66.8 dBA) is the highest location, location 17 (LAeq, 55.7 dBA) is the lowest
location, whereas other locations have sound pressure levels of 57.8 -66.1dBA.
2.2 Soundwalk
In this study, the soundwalk was used to investigate the soundscape naturalness assessment ofsound experience processes. To avoid the effect of other environmental factors (illumination andtemperature), the survey was conducted in June-July 2021, with clear, rain-free weather andtemperatures in the range of 26-28 °C. The survey was prepared at 13:30 on each survey day, and thesoundwalk was conducted from 14:00-16:00 to avoid the effect of lighting and thermal environments(19).
As shown in Figure 1(b), the soundwalk was developed based on the paths that park visitors
frequently visit. The paths were taken as carriers of sound experience processes, where paths 1, 2, and3 are circular. Paths 1 and 2 have the same starting point, however, in opposite directions. Paths 1 and3 have the same direction and different starting and end points. Path 4 and Path 5 are opp osite paths.
To investigate the effects of different sound experience processes on the soundscape, one type oftypical urban park sound experience processes were selected: "sound source processes" (SP). The SP
was defined as the process going from the artificial sound to the natural sound location (from A to N)
and from the natural sound to the artificial sound location (from N to A). At locations 12 (dominatedby artificial sounds) and 15 (dominated by natural sounds), the SPLs are almost the same.
Generally, the number of participants in a soundwalk is between 10 -40 (20, 21). The field survey
recruited 40 volunteers (mean age = 23.2, SD = 2.4), including 23 males and 17 females. All thevolunteers were with normal hearing.The procedure is shown in Figure 1(c). Volunteers were firstdivided into random groups. During the soundwalk, the soundscape naturalness were assessed at thepassing locations in the sound experience processes. After completing each sound experience process,the overall soundscape naturalness assessment were completed. A five-point scale was used to assess
the soundscape naturalness. The soundscape naturalness was assessed ranging from "artificial" (1) to"natural" (5).
2.3 Data Analysis
The results of the soundscape naturalness assessment were analysed using SPSS 24.0. The ShapiroWilk test was used to test the normality distribution of these results. The Wilcoxon signed -rank test
was used to test the differences in the results of soundscape naturalness assessment in different soundexperience processes. Spearman's correlation analysis was used to analyse the correlation between
each location of the soundscape naturalness and the overall soundscape naturalness in each soundexperience process.

3. RESULTS
3.1 The effects of different sound experience processes on the soundscape naturalness
We investigated the effect of different sound experience processes on soundscape naturalness. In
this section, the effects of the sound experience processes on the soundscape naturalness assessment
by selecting five typical paths in the urban park are reported. Path 1 and Path 2 are circular paths withthe same starting points in opposite directions; Path 1 and Path 3 are circular paths with different
starting points in the same direction, whereas Path 4 and Path 5 are on an opposite path but in opposite
directions to each other. We tested whether sound experience process had a different effect on thesoundscape naturalness.

Figure2 Comparison of the soundscape naturalness in different sound experience processes (*p < 0.05, **p< 0.01)Figure 2(d) compares the overall soundscape naturalness of Path 1 with Path 2, Path 1 with Path 3,
and Path 4 with Path 5, showing that the assessment of soundscape naturalness in Path 1 are better
than those in Path 2 (p < 0.05), and the assessment of soundscape naturalness in Path 1 are also better
than those in Path 3(p < 0.05). Remarkably, there are significant differences between soundscapenaturalness in Path 1 and Path 2, and Path 1 and Path 3. It indicating that different sou nd experience
processes affect the assessment of the soundscape naturalness. The soundscape naturalness assessment
were compared through the above three groups of sound experience processes. The results demonstratethat the different sound experience processes affect the soundscape naturalness assessment in theurban park.
However, the reasons behind the differences are not clear. Comparing the soundscape naturalness
assessment through the five sound experience processes (paths) is needed to investigate th e effects ofsound experience process on the soundscape naturalness.
3.2

The effects of peak assessment location on the soundscape naturalness in thesound experience process
To explore the effects of sound experience process on the soundscape naturalness, we further
analysed the results of three sound experience process groups in Section 3.1, considering significant
differences in overall soundscape naturalness.
As shown in Table 1, the relationship between soundscape naturalness of each location and theoverall soundscape naturalness was compared among the three groups of sound experience processes.
Table 1 Relationship between the soundscape naturalness of each location and the overall soundscapenaturalness in the processes with differences
The process of location in the process
Process

Indicator

1

2

3

4

5

6

7

8

9

10

11

12

Location in the process
Path1
Path2
Path1
Path3
Legendsoundscapenaturalness

1

2

3

4

5*

6

7

11

12

13

14**

1**

1

14*

13

12

11

7

6

5

4*

3

2

1**

14**

1**

4

5**

1
soundscapenaturalness
5
Location of negative peakassessment of soundscapenaturalness

2
6

3

4

5*

6

7

11

12

13

7

11

12

13

14**

1

2

3

Location of positive peakassessment of soundscapenaturalnessThe
endsoundscapeassessment

locationofnaturalness*p < 0.05, **p < 0.01
Correlation between soundscape naturalness of each location and overall soundscape naturalness inprocesses
According to Table 1, the lowest of soundscape naturalness location in Path 1 is location 5, and inPath 2 is location 4, whereas highest of soundscape naturalness location in both Path 1 and Path 2 islocation 14. The endpoint of Path 1 and 2 is the same at location 1. A significant difference between
the overall soundscape naturalness of the two sound experience processes ( p < 0.05) was noted. The
difference in Path 1 and Path 2, may be explained by the fact that location 4 in Path 2 is closer to theend of the process than location 5 in Path 1. And the location 14 in Path 1 is closer to the end of theprocess, whereas location 14 in Path 2 is closer to the beginning of the process.
Similarly, the difference in soundscape naturalness in Path 1 and Path 3, may also be explained by
the fact that location 14 in Path 1 is closer than location 14 in Path 3 to the end of the process, andthe location 5 in Path 1 is far to the end of the process, while the location 5 is the end location in Path
3.
This suggests that the differences in the soundscape naturalness between the different processes
shows that the results of the processes are consistent with the peak-end rule. However, whether thesoundscape evaluation at the end of the process is critical to the overall soundscape naturalness, it
still need to be further tested.
3.3 The effects of the sound source process change on the soundscape naturalness
The results from sections 3.1 and 3.2 indicate that the peak assessment of naturalness, which appear
at the end location in the process, have significant effects on the overall soundscape naturalness
assessment.
To further verify the above results, this section investigates the effects of two sound source
processes, artificial-natural sound process and natural-artificial sound process, on the soundscapenaturalness assessment. The results of the assessment of soundscape naturalness in the sound source
processes are compared in Figure 3.

Figure 3 Comparison of soundscape naturalness in sound source processes (*p < 0.05, **p < 0.01; Figure3(b): *p, **p: correlation between soundscape naturalness of each location and overall soundscapenaturalness)
A significant difference in overall soundscape naturalness between the artificial -natural soundprocess and the natural-artificial sound process is shown in Figure 3(a) ( p < 0.05). The difference
may be attributed to the fact that the end location in the artificial-natural sound process is the best
naturalness of soundscape, whereas the end location in the natural-artificial sound process is the worst(Figure 3b).
This again supports the results of section 3.2. The soundscape evaluation at the end of the process
is critical to the overall soundscape evaluation in the process. Thus, the natural sound location is theend location in the sound source process, resulting in a better evaluation of the soundscape.

4. DISCUSSION
By analysing the reason behind the difference in results of overall soundscape naturalness
assessment in the sound experience processes, and the reason for the difference in the soundscapenaturalness assessment in sound source processes, we proposed the above two aspects to impr ove thesoundscape in urban parks. It includes the design of natural sounds and the sound experience process(path).

In the sound experiences processes, the soundscape naturalness assessment of end location affects
the overall soundscape naturalness assessment. The design strategy are proposed around the above
results.
In terms of the design of the sounds, introducing the natural sounds at the end of the soundexperiences processes is beneficial to enhance the naturalness of soundscape. Natural sounds such as
birdsongs, water sounds, as the most perceived sounds, are usually preferred in urban parks. Generally,
the assessment of the naturalness of a soundscape is usually influenced by the perception of naturalsounds. Therefore, natural sounds can be introduced at the end of the sound experience processes inurban parks.
In terms of the design of the sound experience process (path), due to the soundscape naturalness
assessment of end location affects the overall soundscape naturalness assessment. The natural sounds
dominated location should be the end location of the path. Therefore, the natural sounds dominatedlocation is the end location of the path, it should be added in the park to enhance the soundscapenaturalness. Meanwhile, by comparing the soundscape naturalness of two processes in the soundsource process. While it verified the results of section 3.2, the soundscape naturalness of the artificial natural sound process is better than that of the natural-artificial sound process ( p < 0.05). Therefore,
the artificial-natural sound process (path) should be added in the park.
Both of the above measures can increase the soundscape naturalness and further enhance thenatural perception of the urban park. Whereas the soundscape naturalness is usually also influenced
by the visual environment. Thus, different characteristics of visual environments or factors are taken
into account in the following study. Meanwhile, different social contexts are usually affect thesoundscape assessment. The role of differences in social contexts should be further considered in the
future.

5. CONCLUSIONSThis study investigated the mechanism of the effect of the sound experience process on thesoundscape naturalness in an urban park, and the following conclusions can be concluded.
(1) Different sound experience processes have effects on the soundscape naturalness in the urban
park. Influenced by different sound experience processes, the assessment of soundscape naturalness
showed significant differences.
(2) In urban parks, the soundscape naturalness assessment is consistent with the peak-end rule. The
location where the peak assessment of soundscape naturalness appears in the end of the soundexperience process is critical to the overall soundscape naturalness assessment.
(3) Comparison of the sound source process (artificial-natural sound/ natural-artificial sound), thesoundscape naturalness of the artificial-natural sound process is better than that of the naturalartificial sound process, with a mean difference of 0.325, and a median difference of 1.0 ( p < 0.05).
Furthermore, in the stage of planning, increasing the artificial-natural process (path) can improve thequality of soundscape.

ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China (NSFC) [grant
numbers 51878210, 51678180, and 51608147], the Open Projects Fund of Key Laboratory of Ecology
and Energy-saving Study of Dense Habitat (Tongji University), Ministry of Education (2020030103),
and Natural Science Foundation of Heilongjiang Province [YQ2019E022].

REFERENCES
1. Frumkin H, Bratman GN, Breslow SJ, Cochran B, Kahn Jr PH, Lawler JJ, Wood, SA. Nature contact andhuman health: A research agenda. Environmental Health Perspectives, 2017;125(7):075001.
2. Chen Z, Zhai X, Ye S. A Meta-analysis of Restorative Nature Landscapes and Mental Health Benefits on
Urban Residents and Its Planning Implication. Urban Planning International, 2016;31(4):16-43.
3. Herzog TR, Black AM, Fountaine, KA, Knotts DJ. Reflection and attentional recovery as distinctive
benefits of restorative environments. Journal of Environmental Psychology, 1997;17(2):165-170.
4. Tennessen CM, Cimprich B. Views to nature: Effects on attention. Journal of Environmental Psychology,
15(1): 77-85.
5. Fisher JC, Irvine KN, Bicknell JE, Hayes WM, Fernandes D, Mistry J, Davies ZG. Perceived biodiversity,

sound, naturalness and safety enhance the restorative quality and wellbeing benefits of green and blue
space in a neotropical city. Science of the Total Environment, 2021,755:143095.
6. Overvliet K, Soto-Faraco S, Whitaker A, Sorensen L, Mcglone F, Van der Heijden G. Measuring perceptionof naturalness. Proceedings of Measuring Behavior 2008, 6th International Conference on Methods andTechniques in Behavioral Research; 26-29 August, 2008, Maastricht, The Netherlands, p. 86-7
7. Kahneman D, Fredrickson BL, Schreiber CA, Redelmeier DA. When more pain is preferred to less:
Adding a better end. Psychological science. 1993,4(6):401-405.
8. Burt, C. D., Kemp S, Conway, M. Themes, events, and episodes in autobiographical memory. Memory &Cognition, 2003,31:317–325.
9. Cojuharenco I, Ryvkin D. Peak-End rule versus average utility: How utility aggregation affects evaluationsof experiences. Journal of Mathematical Psychology, 2008,52:326–335.
10. Baumgartner H, Sujan M, Padgett D. Patterns of affective reactions to advertisements: The integration of
moment-to-moment responses into overall judgments. Journal of Marketing Research, 1997,34:219–232.
11. Diener F, Wirtz D, Oishi S. End effects of rated life quality: The James Dean effect. Psychological Science,
2001,12:124–128.
12. Chow SM, Ram, N, Boker, SM, Fujita F, Clore GC. Emotion as a thermostat: representing emotion
regulation using a damped oscillator model. Emotion. 2005,5(2):208–225.
13. Arnberger A, Schneider IE, Ebenberger M, Eder R, Venette RC, Snyder SA, Cottrell S. Emerald ash borer
impacts on visual preferences for urban forest recreation settings. Urban Forestry & Urban Greening.
2017,27:235–245.
14. White MP, Cracknell D, Corcoran A, Jenkinson G, Depledge MH, Do preferences for waterscapes persist
in inclement weather and extend to sub-aquatic scenes? Landscape Research. 2014,39(4):339–358
15. Zhang Z, Qie G, Wang C, Jiang S, Li X, Li M. Relationship between forest color characteristics andscenic beauty: Case study analyzing pictures of mountainous forests at sloped positions in Jiuzhai Valley,
China. Forests. 2017,8(3):63.
16. Ibarra FF, Kardan O, Hunter, MR, Kotabe, HP, Meyer, FA, Berman, MG. Image feature types and theirpredictions of aesthetic preference and naturalness. Frontiers in Psychology, 2017,8:1–16.
17. Li H, Xie H, Woodward G. Soundscape components, perceptions, and EEG reactions in typicalmountainous urban parks. Urban Forestry & Urban Greening, 2021, 64:127269.
18. Zhang D, Zhang M, Liu D, Kang J. Soundscape evaluation in han Chinese Buddhist temples. Applied
Acoustics, 2016,111:188-197.
19. Meng Q, Zhao T, Kang J. Influence of music on the behaviors of crowd in urban open public spaces.
Frontiers in psychology, 2018, 9:596.
20. Payne SR. The Production of a Perceived Restorativeness Soundscape Scale. Applied Acoustics, 2013,
74(2):255-263.
21. Nilsson ME, Jeon JY, Rådsten-Ekman M, Axelsson Ö, Hong JY, Jang HS. A soundwalk study on therelationship between soundscape and overall quality of urban outdoor places. The Journal of the
Acoustical Society of America, 2012, 131(4):3474.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0193

A Study of the effect of music sound-related activities in urban open spaces
Chang Miao1; Qi Meng2;
1,2

Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,

Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology,
Harbin 150001
66 West Dazhi Street, Nan Gang District, Harbin, China

ABSTRACTThe sound of music plays an important role in urban open spaces, yet there are limited studies on thecharacteristics and effect of music sound-related activities. Music sound-related activities are defined as
people performing with music. Therefore, the aim of this study is to explore how music sound-related
activities influences people behaviors in urban open spaces. Two typical squares in Harbin, China, wereselected for on-site observation. Two typical types of music sound-related activities, which are singing anddancing, were selected. The result shows that the sound pressure level (SPL) affected the areas coveringmusic sound-related activities, but not the number of participants of the music sound-related activities. The
number of groups of music sound-related activities affected the total number of people and the percentage ofdifferent behaviors of people in the square. The number of singing groups increased the total number ofpeople but decreased when the number of dance groups was larger than three groups. At the same time, thenumber of singing groups has a greater effect on the percentage of people exercising and sitting and thenumber of dance groups has a greater effect on the percentage of people sitting and walking.
Keywords: Soundscape, Music sound-related activity, Behavior

1. INTRODUCTION
Urban open space is a broad concept that points to places where everyone is open and allows awide range of activities. Urban open space can provide residents with three benefits: recreation,
ecology, and esthetic value (1). Sound quality is recognized as one of the key factors for thesustainable development of urban open space (2).Currently the effect of soundscape on the living
environment, the rehabilitation effect of mental illness, and the impro vement of regional
environmental value have gradually become research hotspots for healthy cities (3). International
standard ISO 12913-1 (2014) defines soundscapes as “acoustic environment as perceived orexperienced and/or understood by a person or people, in context” (4). However, with the emergenceof new types of outdoor activities for residents, the existing soundscapes in the open space s of thecities have changed greatly compared with the past. Therefore, it is very important to study the effectof people’s activities and behavior on soundscape in urban open spaces.
In previous studies, it has been found that people are often attracted by various activities in urban
public open spaces, and external stimuli (such as the sound of activity) can provide strangers with thepossibility of socializing(5). Music sound-related activities are obviously easier to be noticed, because
they can increase the sound level from 10.8 to 16dBA(6).In China, Square dancing has gainedwidespread popularity as a social activity that is both enjoyable and healthy (7). Also playing music
and singing songs has been shown to promote mental health, and certain music activities can improve
humans emotional level (8). Music activities have had a positive effect on the psychological andphysical health of urban residents in many aspects. Previous studies have shown that music activities
can cause a significant increase in the SPL, which in turn can affect the sound comfort of different
12

miaochang@hit.edu.cn
mengq@hit.edu.cn（corresponding author）

behavioral groups(6). However, the relationship between the behavioral characteristics and SPL ofmusic activities have not been studied in depth, hereby giving rise to the relevance of this research.
The sound of music is a common sound source in urban open spaces, and the music can influencepeople’s behavior. Some studies have focused on the effect of sound of music on walking speed andtime. For example, a study showed that fast music can increase the walking speed, while slow music
can lead to decrease in walking speed (9). Another study have proven that the sound of music canprolong the duration of stay in a tunnel compared to silence and also proposes that soundscapeplanning can change public behaviors(10). In addition to the above studies,(11) has focused on theinfluence of music sound on walking paths and sitting density. The result showed that the path ofpasserby in an urban area leisure square with music was more centralized than areas without music.
Also with the presence of music, it was observed that as the distance from the sound source increases,
the density of people sitting decreased accordingly (11). Unlike the influence of music, the audiovisual characteristics of music sound-related activities can cause the gathering of people in space. At
the same time, music sound-related activities affected people’s perception and the way they interact
with their surroundings(6, 8). Music sound-related activities will increase space vitality to some extent
but exceeding a certain number may have a negative effect on space. Although some studies havebeen carried out on the effect of music sound-related activities on people’s behaviors, it was only for
one group of music sound-related activity. But multiple groups of music sound-related activities
coexist in the public open space and studies on the effect on crowd behaviors in this situation is still
limited.
Therefore, the purpose of this study is to determine the characteristics and influence of music
sound-related activities. The first step of this study was to determine the relationship between the
SPL of music sound-related activities and its area and the number of participants. The next step was
to determine the effect of groups of music sound-related activity groups on the people in the square.

2. Methodology
2.1 Survey Sites
Harbin is a typical historical city in China, with a rich musical culture. As result of Harbin’s richfoundation for musical activities, in June 2010, the United Nations awarded Harbin the title ‘music
city’. Since some previous studies indicated that environment or space differences may lead todifferent evaluation of the soundscape(6, 11, 12), the effect of musical activities should be studied in
the same environment and places. Considering the types of musical activities, two typical squares in
Harbin were selected as the sites for the study. The first survey site is the playground of Harbin
Institute of Technology (Square A) and an urban leisure square named “LANDSCAPE” square(Square B). The Fig.1 shows that the location of case sites and behaviors map. In the behavior map,the red dots represent the people participating in music-related activities, the green dots represent thepeople exercising, the pink dots represent the people sitting, and the blue dots represent the people
walking.
The two squares were chosen based on the three reasons. (1) That both spaces are approximately
7,000 square meters in size for musical activities and approximately rectangular . (2) The two squares
contain only one type of music sound-related activity (3) The music sound-related activities in thetwo squares are spontaneously formed by people. The music-related activities in square A includes
different groups of people singing. Such music-related activities are centered on performers forming
relatively static space between the audience and the performers. The music-related activities in square
B includes square dance, which is more popular in China. This kind of music -related activities
involves the participants moving within a certain range in a certain formation. What needs to be
specified is the collection range of the behavior of people in square A. All types of musical activities
and the data collected for the current study were limited in the rectangular area of square A.

(a)

(b)
Figure 1–The case sites and behaviors map: (a) Square A:Singing music activities;(b) SquareB:Dancemusic activities
2.2 Measurement of behaviors and SPL
Survey methods in previous soundscape studies included questionnaires and observations. The
questionnaires were mainly about indicators of subjective evaluation, including sound comfort andsubjective loudness(6, 13, 14). However, this study was primarily designed to examine the behavioral
characteristics and effects of music-related activities, which could not be assessed by using
questionnaires. A non-interventional observation method was used in this study. To avoid bias during
the observation process, unmanned aerial vehicle (UAV) was used. The flight altitude of the UAV
was 100m, so that the noise from the UAV would not affect the behaviors of people in the square(15).
To avoid the influence of trees or building shadows on the overhead view, 2 handhelds zoom gimbal
cameras were used at a diagonal while the aerial photography was taken. Each video taken by the
UAV lasted 15-20 minutes each and a photograph was taken every 1min while filming. The behavioral
characteristics of the music-related activity and the effect on the behaviors of other people in thesquare were extracted and summarized using laboratory playback footage combined with field survey
notes. Since this study used a non-interventional observation method, the behaviors of the people
being observed was not affected and the results are reliable.
When the number of music-related activity groups in the square was relatively stable, then thecalibrated BSWA801 was used to measure the SPL of different active music loudspeakers. The SPL
measured 1m away from the music loudspeakers. The level meters were set in slow-mode and Aweight, and reading were acquired every 5s. A total of 5 min data were obtained in each music -related
activity.

3. Results
3.1 Influence of SPL on music-related activities
After detailed measurements and observations of the live music-related activities, it was observedthat square A could have up to six groups and square B could have up to four groups at the same timein the square. Since the relationship between the SPL and the area occupied by music activities andthe number of participants is discussed in this section, thus the scope of these two concepts is
explained. For singing music-related activities in square A, its floor area in this context refers to thesize of the area surrounded by performers and audiences. Its participants include the total number of
performers and audiences. For dance music-related activities in square B, its floor area refers to thearea occupied by the queue of dancing people. Participants in dance music-related activities andsinging music-related activities are defined differently. For dance participants, they are people who
dance with the music excluding the audience.
In terms of the effect of the SPL on the floor area of music activities, the floor area of music
activities increased with the increase of SPL. For singing music activities, the louder the soundpressure level of the music, the farther away the audience and the performer, thus forming a larger
viewing area (Fig.2a). However, the area did not increase indefinitely. The first reason for limited
increase in area was that the SPL of singing performers in the square generally did not exceed 85dBA.
The second reason was that audience wanted to see more details of the performers, so the distance
between the audience and the performer was within a certain range. In this study, it was found thatthe viewing distance of the audience was between 3.2m-8.3m, which is like (16) findings. This study
proposed that the public distance in urban open space at 3.75-7.6 ,which was the boundary between
the face and the body became indistinguishable (16). For dance music-related activities, thedistribution area of the population was positively related to the SPL which allowed all the dancers tohear music (Fig.2b) . This result was consistent with the previous conclusion that the range of square
dance activities in the urban open space had a positive effect on the SPL when close to the music
loudspeaker (17).

(a)(b)
Figure 2–The relationship between SPL and area of music-related activity: (a)Square A(Singing
music activity);(b)Square B(Dance music activity)
In terms of the effect of the SPL on the number of participants in music-related activities, Fig.3
shows that there is not any certain regularity. For singing music activities, the number of people whostay and watch is influenced by personality, experience, and the content of the activities. Therefore,
the number of audiences is uncertain, and the SPL has little effect on it. For dance music activities,
the number of participants is normally a fixed group, but during the dance there is a certain amountof people coming from passerby joining the dance randomly. So, the SPL also has been little influenceon the participants of dance music-related activities.

(a)(b)
Figure 3–The relationship between SPL and number of participants of music activity: (a)Square A(Singing music activity) ;(b) Square B(Dance music activity)
3.2 Influence of music activity groups on people’s behaviors
In terms of the effect of music activity groups on the total number of people and percentage of
people with different behaviors in the square, there were certain differences. As the number of musicrelated activity groups increased, the number of people in square A showed a continuous upward trend
as shown in Fig.4a, while the total number of people in square B showed a trend of increase first andthen decrease as the number of activity group was more than three shown in Fig.4b. This shows thatdifferent types of music-related activities have different attractions to the crowd. For singing musicrelated activities, the audience can volunteer to sing on stage and become performers. Performers andaudiences resonate more, so such music activities will attract more people to the square. For dancemusic-related activities, the participants are basically regular people, and the content of daily danceis basically the same, so such activities are less attractive. In addition, the area of dance music
activities is larger than that of singing music-related activities. When the number of groups ofactivities increased to a certain extent, it affected other active people in the square.

(a)
(b)
Figure 4 –The relationship between the total number of people and number of music activity groups:(a)Square A (Singing music activity) ;(b)Square B(Dance music activity)
In terms of the effect of music-related activity groups on the percentage of people with different
behaviors in the square, the study mainly compared the percentage of people involved in music-related
activities and those who do not participate in music activities which also included exercising, walking
and sitting.

(a)

(b)

(c)
(d)
Figure 5–The percentage of behaviors with different human activities in square A,
where(a)indicates the percentage of standing and watching with singing;( b) indicates the percentageof performing people exercising;(c) indicates the percentage of people sitting;(d) indicates thepercentage of people walking

(a)

(b)

(c)
(d)
Figure 6–The percentage of behaviors with different human activities in square B,
where(a)indicates the percentage of dancer;(b) indicates the percentage of performing exercising;(c)indicates the percentage of sitting;(d) indicates the percentage of walking

The percentage of participants in music-related1 activities increased significantly with the numberof groups, while the non-participants of music-related activities showed an overall decrease. However,the percentage people’s specific behaviors varied between categories. Regarding the behavior of
persons performing exercises, Fig 5b shows that the percentage of people in square A decreased whenthe music-related activity was greater than three groups. The existence of music-related activities insquare B had great effect on the percentage of exercised people, but the increase in the number ofmusic activity groups will not affect the proportion of people exercising, but in increase in the numberof music-related activity groups did not affect the percentage of people exercising as shown in Fig 6b.
Regarding the sitting behavior, Fig 5c shows the same result as people sitting only varies when there
was music activity, and the increase in the number of groups of music-related activity had little effecton it. In square B, the percentage of people sitting in groups zero to three had slight changes but
decreased significantly when it was larger than three groups. This shows that when the dance musicrelated activity is larger than three groups, it will have a greater effect on the people sitting in thesquare. The percentage of people sitting down went from 26.5% to 12.5%. Therefore, it is necessary
to control the number of groups of dance activities in the square. Regarding the walking behavior, Fig
5d shows that the percentage of people walking in square A were not affected by the number of musicrelated activity groups. However, the percentage of people walking in square B declined significantly
when the music-related activity groups was larger than one group as shown in Fig 6d. The percentageof people walking down went from 53.3% to 35.3%. The main behavior groups lead to this difference
when there is no music activity. Through long-term observation, it was found that when there was nomusic activity in square B, the main behavior of people was walking. Therefore, the emergence ofmusic-related activities will have a great effect on the walkers. The above research results also havesome implications for open space planning. When designing the open space, it is necessary to consider
the main behavioral activities of people in the square and reduce the effect of music-related activities
through reasonable site design and planning.

4. CONCLUSIONS
Based on field observations and measurements, this study analyzed the characteristics and effectsof two types of musical activities, with the following conclusions. The SPL of the music activity has
a positive effect on the floor area of the music activity, but the effect on the number of participants
does not show any certain regularity. At the same time, the number of groups for different types ofmusic activities had a different effect on the percentage of people exercising, sitting, and walking inthe square. The number of singing groups had a greater effect on the percentage of people exercisingand sitting, and the number of dance groups had a greater effect on the percentage of people sitting
and walking. Therefore, the SPL and the number of music activity groups can be controlled throughsoundscape planning and design to regulate the behaviors of people in open public space.
In future, studies can discuss the relationship between the SPL and groups of music activates andspatial characteristics, such as morphology, area, and other indicators. In addition, the differences ofthe perception of soundscape amongst people with different activities can also be investigated.

ACKNOWLEDGEMENTS
This study was supported by the National Natural Science Foundation of China （51878210）

REFERENCES
1.Brander LM, Koetse MJ. The value of urban open space: Meta-analyses of contingent valuation andhedonic pricing results. Journal of environmental management. 2011;92(10):2763-73.
2.Zhang Y, Yang Z, Yu X. Measurement and evaluation of interactions in complex urban ecosystem.
Ecological Modelling. 2006;196(1-2):77-89.
3.Kang J, Yang W. Soundscape in urban open public spaces. World Architecture. 2002(6):76-9.
4. International Organization for Standardization (ISO). ISO 12913–1: 2014 Acoustics—Soundscape—Part
1: Definition and Conceptual Framework. ISO, Geneva, Switzerland. 2014.
5.Whyte WH. The social life of small urban spaces. 1980.
6.Meng Q, Kang J. Effect of sound-related activities on human behaviours and acoustic comfort in urban
open spaces. Science of the total environment. 2016;573:481-93.

7.Ou K-l, Wong MYC, Chung PK, Chui KYK. Effect of Square Dance Interventions on Physical and Mental
Health among Chinese Older Adults: A Systematic Review. International Journal of Environmental Researchand Public Health. 2022;19(10):6181.
8.Ho R, Au WT. Effect of Street Performance (Busking) on the Environmental Perception of Public Space.
Frontiers in Psychology. 2021;12:647863.
9.Franěk M, van Noorden L, Režný L. Tempo and walking speed with music in the urban context. Frontiersin psychology. 2014;5:1361.
10.Aletta F, Lepore F, Kostara-Konstantinou E, Kang J, Astolfi A. An experimental study on the influenceof soundscapes on people’s behaviour in an open public space. Applied Sciences. 2016;6(10):276.
11.Meng Q, Zhao T, Kang J. Influence of music on the behaviors of crowd in urban open public spaces.
Frontiers in psychology. 2018;9:596.
12.Kang J, Zhang M. Semantic differential analysis of the soundscape in urban open public spaces. Building& Environment. 2010;45(1):150-7.
13.Ma KW, Mak CM, Hai MW. Effects of environmental sound quality on soundscape preference in a public
urban space. Applied Acoustics. 2021;171:107570.
14.Jo HI, Jeon JY. The influence of human behavioral characteristics on soundscape perception in urban
parks: Subjective and observational approaches. Landscape and Urban Planning. 2020;203:103890.
15.Sinibaldi G, Marino L. Experimental analysis on the noise of propellers for small UAV. Applied Acoustics.
2013;74(1):79-88.
16.Hall ET. The Hidden Dimension: man's use of space in public and private The Bodley Head. London,
Sydney, Toronto. 1969;121.
17.Jingjing Y. Study on the Acoustic Environment of Urban Community Square Based on Element Analysis:
Dalian University of Technology; 2021.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0392

Assessment of a cost-effective headphone calibration procedure forsoundscape evaluations
Bhan LAM; Kenneth OOI; Zhen-Ting ONG;
Karn N. WATCHARASUPAT; Trevor WONG; and Woon-Seng GAN
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore1

ABSTRACT
To increase the availability and adoption of the soundscape standard, a low-cost calibration procedure forreproduction of audio stimuli over headphones was proposed as part of the global “Soundscape Attributes
Translation Project” (SATP) for validating ISO/TS 12913-2:2018 perceived affective quality (PAQ) attributetranslations. A previous preliminary study revealed significant deviations from the intended equivalent continuous A-weighted sound pressure levels (LA,eq ) using the open-circuit voltage (OCV) calibration procedure.
For a more holistic human-centric perspective, the OCV method is further investigated here in terms of psychoacoustic parameters, including relevant exceedance levels to account for temporal effects on the same 27
stimuli from the SATP. Moreover, a within-subjects experiment with 36 participants was conducted to examine the effects of OCV calibration on the PAQ attributes in ISO/TS 12913-2:2018. Bland-Altman analysis of
the objective indicators revealed large biases in the OCV method across all weighted sound level and loudness
indicators; and roughness indicators at 5 % and 10 % exceedance levels. Significant perceptual differences
due to the OCV method were observed in about 20 % of the stimuli, which did not correspond clearly withthe biased acoustic indicators. A cautioned interpretation of the objective and perceptual differences due tosmall and unpaired samples nevertheless provide grounds for further investigation.
Keywords: soundscape, headphones, listening tests

1.

INTRODUCTION

Accurate calibration of recordings and stimuli to desired (1) or in-situ (2) sound pressure levels (SPL) is
crucial to laboratory-based soundscape studies, because differences in SPL may influence ratings of perceptual indicators of the soundscapes under study. For example, preference scores of the same road traffic noise
recording were found to be lower when it was presented at A-weighted equivalent SPL (LA,eq ) of 75 dB, as
compared to when it was presented at 55 dB (3). Similarly, significant differences in subjectively-rated perceived loudness and overall soundscape quality were observed when the same hydraulic breaker sound waspresented at LA,eq values of 55, 65, and 75 dB (4).
While much effort has been put into standardizing the definitions (5), measurement (6), and analysis (7)of soundscapes via the ISO 12913 series of standards, there appears to be scant detail on exact calibration
procedures that should be undertaken to control the SPLs at which stimuli are presented in soundscape studies.
Hence, some studies have used “subjective” calibration methods, where participants adjusted presentation
SPLs on fixed perceptual criteria, whereas others have used “objective” calibration methods, where physicalequipment is used for the calibration procedure instead of human participants.
Sudarsono et al. proposed a “subjective” calibration method in (8), where participants adjusted soundscape recordings to levels that they thought matched the in-situ recording conditions (even though they had
not experienced those conditions themselves). Most participants adjusted them to levels between 5 dB to
15 dB lower than the in-situ levels. However, results from a semantic differential analysis of the adjusted
recordings against in-situ conditions showed no significant difference between the laboratory and in-situ conditions, which suggests that results could be ecologically valid even if reproduced levels differ from in-situ
1 {bhanlam, wooi002, ztong, ylau01, karn001, trevor.wong, ewsgan}@ntu.edu.sg

or “desired” levels. In addition, a study by Kothinti et al. (9) revealed no significant differences in behavioral
salience (the duration which a participant was more attentive to one of two simultaneously-presented stimulli)
measurements between crowdsourced data obtained from participants who adjusted stimuli to a comfortable
listening level and laboratory data obtained from participants who were presented the same stimuli at levels
calibrated by the researchers.
On the other hand, “objective” calibration methods can be automated and may be less tedious to perform
since they do not require the involvement of human participants. Open-circuit voltage (OCV) methods,
where the same gain adjustment is made to all stimuli using a separate reference track based on the rootmean-squared (RMS) voltage across a playback device, are generally quick and economical, since they do
not require specialized equipment beyond the playback equipment already needed for stimuli presentationin the first place. An OCV method was first used by Park et al. to calibrate tracks prior to the extraction of
acoustic parameters for analysis (10), and later independently proposed by Aletta et al. for in use presenting
a standard set of 27 stimuli for validation of candidate translations of perceptual attributes in the SoundscapeAttributes Translation Project (SATP) (11), and Park et al. also previously used an OCV method was tocalibrate tracks prior to the extraction of acoustic parameters for analysis (10).
Alternatively, methods using head-and-torso simulators (HATS) to account for binaural presentation of
stimuli and pinna cues have previously been used by De Coensel et al. (12), and have also been proposedin (13) for automated calibration of soundtracks reproduced over headphones or loudspeakers with binauralsystems compliant with ISO/TS 12913-2:2018. A GRAS 43AG Ear and Cheek simulator was also used forcalibration of stimuli in a study on music perception by McPherson et al. (14). Although such methods canaccount for variability in headphone quality and playback devices, the need for specialized equipment may
make them cost-prohibitive for some research groups.
Therefore, we aim to quantify the differences (if any) in OCV and HATS calibration methods for stimuli
in the context of soundscape studies, and seek to investigate if an OCV method could potentially be a costeffective alternative to a HATS calibration method. In terms of the objectively-measured SPL, a preliminary
study has found differences in SPL between OCV and HATS calibration methods ranging from 2.5 dB to8.75 dB, depending on the stimulus (15). For this study, we provide an overview of the preliminary study, andextend it with additional objective analysis, as well as subjective analysis utilizing perceptual characteristics
according to Part 2 of the Method A questionnaire in ISO/TS 12913-2:2018 (16), in the context of candidate
translations for the Bahasa Melayu version of the questionnaire.

2.

METHODOLOGY

2.1 Stimuli and calibration
Both the subjective and objective analysis are based on a set of 27 audio stimuli that were used in SATP.
All 27 stimuli were 30-s binaural excerpts recorded with a wearable binaural microphone and a data acqusition device (BHS II; SQobold, HEAD acoustics GmbH, Herzogenrath, Germany) according to the protocol
described in (17). These 27 excerpts were then calibrated for headphone playback using one of two calibration methods, i.e. OCV (15) and HATS (18). The calibrated stimuli were derived in a previous study (15)with the exact hardware setup used in this study.
2.2 Objective assessment
Taking reference from the minimum reporting requirements and analysis guidelines for soundscapes (7,16), suggested acoustic and psychoacoustic indicators were selected as a basis for objective comparison
between calibration methods. Acoustic indicators include both A- and C-weighted equivalent continuous
sound pressure levels (19), i.e. LA,eq , LC,eq . Psychoacoustic indicators include loudness (20), sharpness (21),roughness (22), tonality based on Sottek’s hearing model (23), and fluctuation strength (24).
All the acoustic and psychoacoustic indicators were computed for each of the binaural channels acrossall 27 stimuli. In total, 3 sets of parameters were generated, namely of (1) the in-situ binuaral recordings,(2) the binaural recordings of the in-situ tracks reproduced over headphones after calibration with the OCV
method, and (3) the binaural recordings of the in-situ tracks reproduced over headphones after calibration
with the HATS method. Each acoustic parameter was analysed as the mean across both binaural channels.
Additionally, percentage exceedance levels (i.e. {5, 10, 50, 90, 95}%) were also computed to quantify thevariation of the indicators over time (19).

Figure 1: Participant breakdown for subjective assessment of OCV and HATS calibration methods.
2.3 Experimental design of subjective assessment
To assess the perceptual differences between the calibration procedures, a within-subject experimental
design was adopted. Participants would assess two sets of the same 27 stimuli, of which one was OCVcalibrated and the other other was HATS-calibrated. Each set was evaluated in separate sessions spaced atleast one day apart to reduce the impact of memory effects. The participants were blind to the calibration
method used in each session.
The stimuli were assessed based on the eight ISO/TS 12913-2:2018 perceived affective quality (PAQ)
attributes, in Bahasa Melayu (ISO 639-3: ZSM), on a 101-point scale from strongly disagree (0) to strongly
agree (100). The Bahasa Melayu candidate translations used for the subjective assessment for the PAQ
attributes were “menyenangkan” for “pleasant”, “huru-hara” for “chaotic”, “rancak” for “vibrant”, “uneventful” for “tidak meriah”, “calm” for “tenang”, ‘ ‘annoying” for “membingitkan”, “eventful” for “meriah”, and“monotonous” for “membosankan” were respectively (11, 25).
The sliders for all the PAQ attributes were set to a default neutral position (50) and were only activatedafter the stimuli had been played in their entirety at least once. Participants were allowed to repeat listening
to each stimulus as many times as required. The experiment was conducted through a MATLAB-basedgraphical user interface in a quiet room (26), wherein the audio stimuli were presented over studio monitor
headphones (DT 990 Pro, Beyerdynamic GmbH & Co. KG, Heilbronn, Germany) powered by an audio
interface (UltraLite AVB, MOTU, Inc., Cambridge, MA, USA).
Formal ethical approval was sought from the Institutional Review Board (IRB) of NTU (Ref. IRB-2021293) for the responses collected. In compliance with ethical procedures, informed consent was obtained fromall the participants, and also from the parent/guardian if the participant was between 18 and 20 years old.
2.4 Participants
A total of 36 participants [female: 17 (47 %); male: 19 (53 %)] were recruited for this study. However, 2participants were excluded from the study. One participant was excluded due to failing a standard hearing test
(mean threshold of hearing < 20 dB HL at 0.5, 1, 2, 4 kHz) administered to all participants via an audiometer(AD629, Interacoustics A/S, Middelfart, Denmark), and another participant was excluded due to technicalissues with the setup occurring within the duration of the experiment that could have led to results being
unreliable. Hence, responses from a total of 34 participants [female: 16 (47 %); male: 18 (53 %)] were used
for the final data analysis. The ages of these 34 participants ranged from 19 to 45 years (x̄age = 25.7 years,
SDage = 6.7, SEage = 1.17).
In line with ethical guidelines, participants were allowed to terminate the study at any time, and thus only
19 participants [female: 10 (53 %); male: 9 (47 %)] took part in both phases. 13 [female: 5 (38 %); male:
8 (62 %)] participants completed the OCV set only, and 2 participants [female: 1 (50 %); male: 1 (50 %)]completed the HATS set only. A graphical representation of the participant breakdown is shown in Figure 1.
2.5 Data analysis
To account for the unpaired samples when performing the paired comparison between the OCV andHATS calibration methods across each stimulus and PAQ attribute pair, an optimal pooled t-test (OPTT) was
adopted (27, 28).
The acoustic and psychoacoustic indicators were computed with a commercial software package (ArtemiSSUITE, HEAD acoustics GmbH, Herzogenrath, Germany). Bland-Altman (BA) parametric statistics and plots
were generated to examine the agreement between in situ, and OCV and HATS calibrations methods (29).
Additionally, 95 % confidence intervals were computed for the mean to examine the level of bias, and also
for both the upper and lower bounds of the 95 % limits of agreement (LoA) to discover outliers.
All data analyses were conducted with the R programming language (30) on a 64-bit ARM environment. The data that support the findings of this study are openly available in NTU research data repos-

L(A)
20

L5(A)

L10(A)

L50(A)

L90(A)

L95(A)

M + 1.96SD: 13.46

M + 1.96SD: 13.36

M + 1.96SD: 13.06

M + 1.96SD: 12.74

M + 1.96SD: 12.67

M = 3.42

M = 3.41

M = 3.42

M = 3.44

M = 3.48

M = 3.49

M − 1.96SD: −6.38

M − 1.96SD: −6.63

M − 1.96SD: −6.53

M − 1.96SD: −6.19

M − 1.96SD: −5.77

M − 1.96SD: −5.69

M + 1.96SD: 1.47
M = −0.15

M + 1.96SD: 1.35
M = −0.03

M + 1.96SD: 1.46
M = −0.14

M + 1.96SD: 1.86
M = −0.15

M + 1.96SD: 2.4
M = −0.21

M + 1.96SD: 2.46
M = −0.25

M − 1.96SD: −1.78

M − 1.96SD: −1.42

M − 1.96SD: −1.74

M − 1.96SD: −2.15

M − 1.96SD: −2.82

M − 1.96SD: −2.97

M + 1.96SD: 14.36

M + 1.96SD: 14.61

M + 1.96SD: 14.43

M + 1.96SD: 13.94

M + 1.96SD: 13.8

M + 1.96SD: 13.67

M = 2.67

M = 2.78

M = 2.68

M = 2.69

M = 2.67

M = 2.63

M − 1.96SD: −9.02

M − 1.96SD: −9.04

M − 1.96SD: −9.08

M − 1.96SD: −8.57

M − 1.96SD: −8.46

M − 1.96SD: −8.41

10

−10
−20
20
10
0
−10
−20
20

0
−10

In−situ~OCV

10

In−situ~HATS

Difference between methods0

HATS~OCV

M + 1.96SD: 13.22

−20

50

75

0

10

50

75

0
10

50

75

0
10

50

75

0

10

50

75

0

10

50

75

0

10

Arithemic average between methods

Figure 2: Bland-Altman plot of agreement between calibration methods across A-weighted level parameters
in dB(A). The average bias is estimated by the mean of the differences and bounded by the 95 % limits ofagreement (LoA). Confidence intervals are based on 95 % confidence level for both LoAs () and the mean(). The numbers 5, 10, 50, 90, and 95 in the column headers indicate the exceedance levels.
itory DR-NTU (Data) at doi:10.21979/N9/AUE2LL, and the replication code is available on GitHub atgithub.com/ntudsp/ica22-calibration.

3.

RESULTS AND DISCUSSION

3.1 Objective agreement between the OCV and HATS calibration methods
Any bias introduced by the calibration methods during reproduction can be observed through the mean ofthe differences with the in-situ track across all stimuli in the BA plots. To determine outliers, the standard
error was adopted as the limits of agreement (LoA), i.e., d¯+z0.025 ·sd and d¯+z0.975 ·sd for the lower and upper
limits respectively, where z0.025 = −1.96 and z0.975 = 1.96. d¯ and sd are the mean and standard deviation of
the differences, respectively.
The calibration method can be considered unbiased if the line of equality (i.e. zero difference) is within
the 95 % confidence intervals of the mean. Similarly, outliers are determined by differences that fall outside
the lower and upper 95 % confidence intervals of the respective lower and upper LoAs. The two sided 95 %confidence intervals are determined by the t standard error at K − 1 degrees of freedom for small K as
1/2 


sd
1.962
1
· √
+
,
(1)
± t0.975 ·K 2 (N + 1)
K
where t0.975 = 2.056, and K is the sample size (31).
Since the HATS calibration method algorithmically adjusts the LA,eq levels of the stimuli to within 0.5 dB
tolerance of the in situ LA,eq levels (18), it is expected that the differences between HATS and in situ acrossall tracks are near zero, as shown in Figure 2. The narrow intervals were also observed across all A-weighted
percentage exceedance levels. Moreover, no bias and outliers were present. However, the OCV method
produced biases of about d¯(HATS∼OCV) ≈ 3.5 dB(A) and d¯(in-situ∼OCV) ≈ 2.7 dB(A) across all A-weighted SPL
indicators in the respective differences between both HATS and in situ with OCV. The stimulus KT01 was
KT01, LA,eq
an outlier across all A-weighted SPL indicators in differences between in situ and OCV, with d(in-situ∼OCV)=−12.9 dB(A). It is worth noting that due to the high noise floor of the HATS signal chain, the track KT01
failed to calibrate and was omitted from all the BA plots showing a comparison with the HATS calibration
method.
Unsurprisingly, results were similar across the C-weighted SPL (LC,eq ) indicators, with only a slightbias of about d¯(in-situ∼HATS) ≈ 1.2 dB(C) in the differences between in situ and HATS, and biases of about

S

S5

S10

S50

S90

S95

1.0

0.0

M + 1.96SD: 0.14
M = 0.03

M + 1.96SD: 0.14
M = 0.03

M + 1.96SD: 0.19
M = 0.04

M + 1.96SD: 0.21

M + 1.96SD: 0.22

M = 0.04

M = 0.04

M − 1.96SD: −0.1

M − 1.96SD: −0.08

M − 1.96SD: −0.08

M − 1.96SD: −0.11

M − 1.96SD: −0.14

M − 1.96SD: −0.14

M + 1.96SD: −0.02

M + 1.96SD: −0.08

M + 1.96SD: −0.05

M + 1.96SD: −0.01

M + 1.96SD: 0.02

M + 1.96SD: 0.03

M = −0.39

M = −0.36

M = −0.37

M = −0.4

M = −0.4

M = −0.4

M − 1.96SD: −0.64

M − 1.96SD: −0.68

M − 1.96SD: −0.78

M − 1.96SD: −0.82

M − 1.96SD: −0.82

M + 1.96SD: −0.04

M + 1.96SD: −0.1

M + 1.96SD: −0.07

M + 1.96SD: −0.03

M + 1.96SD: −0.02

M + 1.96SD: −0.02

M = −0.36

M = −0.35

M = −0.35

M = −0.37

M = −0.37

M = −0.37

M − 1.96SD: −0.69

M − 1.96SD: −0.6

M − 1.96SD: −0.63

M − 1.96SD: −0.71

M − 1.96SD: −0.73

M − 1.96SD: −0.72

−1.0
1.0
0.5
0.0

In−situ~HATS

Difference between methods−0.5

M + 1.96SD: 0.18
M = 0.04

HATS~OCV

0.5

−0.5
−1.0

M − 1.96SD: −0.76

1.0

0.0

In−situ~OCV

0.5

−0.5
−1.0

0

1

2

3 0

1

2

3 0

1

2

3 0

1

2

3 0

1

2

3 0

1

2

3

Arithemic average between methods

Figure 3: Bland-Altman plot of agreement between calibration methods across psychoacoustic sharpness
parameters in acum. The average bias is estimated by the mean of the differences and bounded by the 95 %limits of agreement (LoA). Confidence intervals are based on 95 % confidence level for both LoAs () andthe mean (). The numbers 5, 10, 50, 90, and 95 in the column headers indicate the exceedance levels.
d¯(HATS∼OCV) ≈ 2.5 dB(C) and d¯(in-situ∼OCV) ≈ 3 dB(C) in the respective differences between both HATS and
KT01, LC,eqin situ with OCV, and a clear outlier in KT01 with d(in-situ∼OCV)= −13.5 dB(C).
Psychoacoustic loudness (N) indicators also exhibited similar trends to the weighted SPL indicators,
whereby biases of about d¯(HATS∼OCV) ≈ 3 soneGF and d¯(in-situ∼OCV) ≈ 2 soneGF were observed in both the
HATS and in situ differences with OCV, respectively. The stimulus CT308 was the clear outlier in both theCT308, N
CT308, N
HATS (d¯(HATS∼OCV)= −28.6 soneGF) and in situ (d(in-situ∼OCV)= −27.5 soneGF) differences with OCV.
Owing to the similarities with the A-weighted SPL indicators, both the BA plots of the C-weighted SPL
indicators and psychoacoustic loudness indicators were omitted for brevity.
The BA plots of the psychoacoustic sharpness indicators (S) indicated similar biases introduced by boththe HATS and OCV calibrations, as shown in Figure 3. A clear outlier in the stimulus VP01b was observed
VP01b, S
VP01b, Sin both HATS (d(in-situ∼HATS)= −1.05 acum) and OCV (d(in-situ∼OCV)= −0.893 acum) differences with insitu. The differences were similar across all percentage exceedance levels of sharpness. The absence of bias
and narrow intervals between HATS and OCV suggests that the effect of sharpness could be attributed tothe acoustic effects of the HATS signal chain. Based on inspection of the frequency spectrum, there was an
overall increase in the noise floor, which could be attributed to the inability of the headphone in reproducing
the dominant low-frequency content in VP01b.
Biases in psychoacoustic roughness R were also introduced by both HATS and OCV calibration methods, as shown in Figure 4. The bias of d¯(in-situ∼HATS) = 0.01 asper was consistent throughout all roughness
indicators in the differences between in situ and HATS, whereas the biases observed in the 5 % and 10 %exceedance levels were double (i.e. d¯(in-situ∼HATS) = 0.02 asper) of that in the other roughness indicators in
CT308, R
the differences between in situ and OCV. Moreover, CT308 (d(in-situ∼HATS)= 0.022 asper) was found to be

W09, R
the outlier in differences between in situ and HATS, whereas W09 (d(in-situ∼OCV)= 0.0334 asper) was the outlier in the difference between in situ and OCV across all roughness indicators. No biases nor outliers wereobserved in the differences between HATS and OCV in all roughness parameters.
Since no bias and outliers were observed across both HATS and OCV methods in tonality and fluctuation
strength indicators, the BA plots were omitted for conciseness. All in all, any possible perceptual differences between the OCV and HATS calibration methods could be attributed to differences in weighted SPL
indicators and psychoacoustic loudness.

R

R5

R10

R50

R90

R95

0.06

0.00

M + 1.96SD: 0.01

M + 1.96SD: 0.01

M=0

M=0

M=0

M + 1.96SD: 0.01
M=0

−0.03

M − 1.96SD: −0.01

M − 1.96SD: −0.02

M − 1.96SD: −0.01

M − 1.96SD: −0.01

M − 1.96SD: −0.01

M − 1.96SD: −0.01

−0.06
0.06
0.03

M + 1.96SD: 0.03

M = 0.01

M = 0.01

M + 1.96SD: 0.02
M = 0.01

M + 1.96SD: 0.01
M = 0.01

M + 1.96SD: 0.01
M = 0.01

M − 1.96SD: 0

M − 1.96SD: 0

M − 1.96SD: 0

M − 1.96SD: 0

M − 1.96SD: 0

M − 1.96SD: 0

M + 1.96SD: 0.04

M + 1.96SD: 0.04

M = 0.02

M + 1.96SD: 0.02

M = 0.02

M + 1.96SD: 0.02

M = 0.01

M = 0.01

M = 0.01

M − 1.96SD: −0.01

M − 1.96SD: 0

M − 1.96SD: 0

M − 1.96SD: 0

0.00
−0.03
−0.06
0.06
M + 1.96SD: 0.03

0.03

M = 0.01

M + 1.96SD: 0.03

0.00
M − 1.96SD: 0−0.03

M − 1.96SD: −0.01

In−situ~OCV

M + 1.96SD: 0.03
M + 1.96SD: 0.02
M = 0.01

In−situ~HATS

Difference between methods

M + 1.96SD: 0.02

M=0

HATS~OCV

M + 1.96SD: 0.02

M=0

M + 1.96SD: 0.02

0.03

−0.06

.050.00 0.05 0.10 0.15 0.200.050.00 0.05 0.10 0.15 0.200.050.00 0.05 0.10 0.15 0.200.050.00 0.05 0.10 0.15 0.200.050.00 0.05 0.10 0.15 0.200.050.00 0.05 0.10 0.15 0.20
−
−
−
−
−

−0

Arithemic average between methods

Figure 4: Bland-Altman plot of agreement between calibration methods across psychoacoustic roughness
parameters in asper. The average bias is estimated by the mean of the differences and bounded by the 95 %limits of agreement (LoA). Confidence intervals are based on 95 % confidence level for both LoAs () andthe mean (). The numbers 5, 10, 50, 90, and 95 in the column headers indicate the exceedance levels.
3.2 Effect of calibration on perceived affective quality
A within-subject design was employed to investigate the perceptual differences between the HATS andOCV calibration methods. As mentioned in Section 2.5, presence of unpaired samples due to participant
dropout was managed by adopting a t-test for partially paired data, i.e. optimal pooled t-test (OPTT) (27). A
total of Nstimuli × NPAQ = 27 × 8 = 216 stimuli-attribute paired comparisons were made, and with Bonferroni
corrections applied to the resultant p-values. The mean of the differences between the calibration methods
for each pair, as well as the significance of the adjusted p-values are visualized in a heatmap, as shown in
Figure 5.
Calmness and pleasantness were significantly different in 5 stimuli (i.e. CG07, E12b, CT308, E05, E10).
Except for E05, four of those stimuli had significant differences in at least one other attribute. For LS06
and E02, significant differences were only observed in annoying and eventful, respectively. The differencesin weighted SPL and psychoacoustic loudness indicators did not seem to affect all the stimuli equally. For
instance, stimuli with the largest differences in A-weighted SPL (e.g. W23a, CG01, W11a, N1), C-weighted
SPL (e.g. CG01, W11a, OS01d), and loudness (e.g. W09, OS01d, E11b) were not amongst those stimuli that
registered significant perceptual differences. Hence, interaction effects between indicators could be present
and warrants further investigation.

4.

CONCLUSION

A proposed low-cost OCV calibration procedure for reproduction of audio stimuli over headphones
was examined objectively and perceptually, in comparison with a calibrated HATS, in the context of ISO
12913 soundscape evaluations. The OCV method differed from the dB(A)-based HATS method in objective
weighted SPL, loudness, R5 and R10 indicators, but was similar in sharpness, tonality, fluctuation strength,
and the rest of the roughness indicators across 27 distinct audio stimuli. Significant perceptual differenceswere observed for a small number via paired comparison analysis of stimuli that did not immediately correspond to specific objective differences.
However, the interpretations of the perceptual effects could be modulated by the small sample size, unpaired samples, and inherent intricacies due to translation of PAQ attributes from English to Bahasa Melayu.
Nevertheless, the significant differences observed in the main axis PAQ attributes provides preliminary evidence of the potential perceptual differences due to calibration methods for laboratory assessments of soundscape.

ISO/TS 12913−2 Perceived affective quality attributes

t

ran

vib

l

tfu

un

en
ev

t

an

as

ple

ton

*** *** * ** **Mean ofdifferences
60

s

ou

***

o
on

30

m

tfu

en
ev

ch

l

c
oti

***

lm

*** *** * *** **

a

ca

g

yin
no

an

0

***

***

**

−30
−60

*

*

**

07 2b 308 05 10 02 N1 01b T01 S06 11b R01 09 PJ01 G04 11a S01d 06 G01 01b 15 09 23a S01c 01 22 16
W R
W C VP W E W O
W W W
E K L
E H
CG E1 CT E E E
C W OStimuli from SATP

Figure 5: Heatmap indicating the estimated mean of the differences between the OCV and HATS calibrationfor each stimulus-attribute pair based on the optimal pooled t-test with Bonferroni correction. Significancelevels at {5, 1, 0.1, 0.01}% are symbolized by {∗, ∗∗, ∗ ∗ ∗, ∗ ∗ ∗∗}, respectively. Stimuli are ranked by theabsolute mean differences across all attributes.

ACKNOWLEDGEMENTS
This research is supported by the Singapore Ministry of National Development and the National Research
Foundation, Prime Minister’s Office under the Cities of Tomorrow Research Programme (Award No. COTV4-2020-1). Any opinions, findings and conclusions or recommendations expressed in this material arethose of the authors and do not reflect the view of National Research Foundation, Singapore, and Ministry of
National Development, Singapore.

REFERENCES
1.

Galbrun L, Ali TT. Acoustical and perceptual assessment of water sounds and their use over road traffic
noise. The Journal of the Acoustical Society of America. 2013 1;133(1):227–237.
2. Aletta F, Kang J. Towards an Urban Vibrancy Model: A Soundscape Approach. International Journal ofEnvironmental Research and Public Health 2018, Vol 15, Page 1712. 2018 8;15(8):1712.
3. Jeon JY, Lee PJ. Evaluation of urban space as a concept of soundscape. In: Griefahn B, editor. Proceedings of the Institute of Acoustics Volume 33 Pt.3. ICBEN 2011. London, UK: Institute of Acoustics(IOA); 2011. p. 743–751.
4. Hong JY, Ong ZT, Lam B, Ooi K, Gan WS, Kang J, et al. Effects of adding natural sounds to urban
noises on the perceived loudness of noise and soundscape quality. Science of the Total Environment.
2020;711.
5. International Organization for Standardization. ISO 12913-1:2014 - Acoustics - Soundscape - Definitionand conceptual framework. Geneva: International Organization for Standardization; 2014.
6. International Organization for Standardization. ISO/TS 12913-2:2018 - Acoustics — Soundscape —Part 2: Data collection and reporting requirements. Geneva, Switzerland: International Organization forStandardization; 2018.
7. International Organization for Standardization. ISO/TS 12913-3:2019 - Acoustics — Soundscape - Part
3: Data analysis. International Organization for Standardization; 2019.
8. Sudarsono AS, Lam YW, Davies WJ. The effect of sound level on perception of reproduced soundscapes.
Applied Acoustics. 2016;110:53–60. Available from: http://dx.doi.org/10.1016/j.apacoust.2016.03.011.
9. Kothinti SR, Huang N, Elhilali M. Auditory salience using natural scenes: An online study. The Journalof the Acoustical Society of America. 2021;150(4):2952.
10. Park M, Kohlrausch A, De Bruijn W, De Jager P, Simons K. Source-specific analysis of the noise in anintensive care unit. In: Proceedings of Inter-Noise 2013; 2013. p. 5186–5194.
11. Aletta F, Oberman T, Axelsson O, Xie H, Zhang Y, Lau SK, et al. Soundscape assessment: Towards avalidated translation of perceptual attributes in different languages. In: Proceedings of Inter-Noise 2020;
2020. .
12. De Coensel B, Vanwetswinkel S, Botteldooren D. Effects of natural sounds on the perception of roadtraffic noise. JASA Express Letters. 2011;129(4):148–153.
13. Ooi K, Xie Y, Lam B, Gan WS. Automation of binaural headphone audio calibration on an artificial
head. MethodsX. 2021;8(February):1–12. Available from: https://doi.org/10.1016/j.mex.2021.101288.
14. McPherson MJ, Dolan SE, Durango A, Ossandon T, Valdés J, Undurraga EA, et al. Perceptual fusionof musical notes by native Amazonians suggests universal representations of musical intervals. Nature
Communications. 2020;11(1):1–14. Available from: http://dx.doi.org/10.1038/s41467-020-16448-6.
15. Lam B, Ooi K, Watcharasupat KN, Ong ZT, Lau YT, Wong T, et al. Preliminary assessment of a costeffective headphone calibration procedure for soundscape evaluations. arXiv. 2022 5. Available from:http://arxiv.org/abs/2205.04728.
16. International Organization for Standardization. ISO/TS 12913-2: Acoustics — Soundscape — Part
2: Data collection and reporting requirements. Geneva, Switzerland: International Organization forStandardization; 2018.
17. Mitchell A, Oberman T, Aletta F, Erfanian M, Kachlicka M, Lionello M, et al. The Soundscape Indices (SSID) Protocol: A Method for Urban Soundscape Surveys—Questionnaires with Acoustical andContextual Information. Applied Sciences. 2020 4;10(7):2397.
18. Ooi K, Xie Y, Lam B, Gan WS. Automation of binaural headphone audio calibration on an artificial
head. MethodsX. 2021;8:101288.
19. International Organization for Standardization. ISO 1996-1 Acoustics — Description, measurement andassessment of environmental noise — Part 1: Basic quantities and assessment procedures. Geneva,
Switzerland: International Organization for Standardization; 2016.
20. International Organization for Standardization. ISO 532-1:2017 - Acoustics — Method for calculating
loudness — Part 1: Zwicker method. vol. 7. Geneva: International Organization for Standardization;
2017.
21. German Institute for Standardisation (Deutsches Institut für Normung). DIN 45692 - Measurement technique for the simulation of the auditory sensation of sharpness (In German). August. Berlin, Germany:
German Institute for Standardisation (Deutsches Institut für Normung); 2009.
22. Ecma International. ECMA-418-2:2020 - Psychoacoustic metrics for ITT equipment - Part 2 (models
based on human perception). 1st ed. Geneva, Switzerland; 2020.
23. Ecma International. ECMA-74 - Acoustics - Measurement of airborne noise emitted by information
technology and telecommunications equipment. 19th ed. Geneva, Switzerland; 2021.
24. Fastl H, Zwicker E. Psychoacoustics - Facts and Models. Huang TS, Schroeder MR, Kohonen T, editors.
Springer; 2001.
25. Lam B, Chieng J, Watcharasupat KN, Ooi K, Ong ZT, Hong JY, et al. Crossing the Linguistic Causeway: A Binational Approach for Translating Soundscape Attributes to Bahasa Melayu. arXiv. 2022 6.
Available from: http://arxiv.org/abs/2206.03104.
26. Ooi K, Ong ZT, Watcharasupat KN, Lam B. GUI for SATP Validation Experiments; 2021. Available
from: https://github.com/kenowr/satp-gui.
27. Guo B, Yuan Y. A comparative review of methods for comparing means using partially paired data. Statistical Methods in Medical Research. 2017 6;26(3):1323–1340. Available from: http://journals.sagepub.
com/doi/10.1177/0962280215577111.
28. Henriksen AA. T-test for partially paired data; 2018. Available from: https://gist.github.com/AkselA/
9dff13ef662f64188f708c22e0d455ad.
29. Martin Bland J, Altman D. Statistical methods for assessing agreement between two methods of clinicalmeasurement. The Lancet. 1986 2;327(8476):307–310. Available from: https://linkinghub.elsevier.com/
retrieve/pii/S0140673686908378.
30. R Core Team. R: A language and environment for statistical computing. R Foundation for StatisticalComputing. Vienna, Austria: The R Foundation for Statistical Computing; 2021. Available from: https://www.r-project.org/.
31. Bland JM, Altman DG. Measuring agreement in method comparison studies. Statistical Methods in
Medical Research. 1999 4;8(2):135–160.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0421

Do uHear? Validation of uHear App for Preliminary Screening of
Hearing Ability in Soundscape Studies
Zhen-Ting ONG1,* ; Bhan LAM1,* ; Kenneth OOI1 ;Karn N. WATCHARASUPAT1 ; Trevor WONG1 ; Woon-Seng GAN1
1 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

ABSTRACT
Studies involving soundscape perception often exclude participants with hearing loss to prevent impaired perception from affecting experimental results. Participants are typically screened with pure tone audiometry, the“gold standard” for identifying and quantifying hearing loss at specific frequencies, and excluded if a studydependent threshold is not met. However, procuring professional audiometric equipment for soundscapestudies may be cost-ineffective, and manually performing audiometric tests is labour-intensive. Moreover,testing requirements for soundscape studies may not require sensitivities and specificities as high as that ina medical diagnosis setting. Hence, in this study, we investigate the effectiveness of the uHear app, an iOS
application, as an affordable and automatic alternative to a conventional audiometer in screening participants
for hearing loss for the purpose of soundscape studies or listening tests in general. Based on audiometric comparisons with the audiometer of 163 participants, the uHear app was found to have high precision (98.04 %)when using the World Health Organization (WHO) grading scheme for assessing normal hearing. Precision
is further improved (98.69 %) when all frequencies assessed with the uHear app is considered in the grading,which lends further support to this cost-effective, automated alternative to screen for normal hearing.
Keywords: soundscape, audiometry, listening tests

1.

INTRODUCTION

In the normative minimum reporting requirements of soundscape studies, the hearing ability of all humanparticipants must be explicitly reported (1). Moreover, the required computation of psychoacoustic loudnessof the acoustic environment represents an “average loudness as perceived by a group of individuals withotologically normal hearing” (2). Despite the inherent requirements to determine normal hearing ability inlistening experiments, there are no explicit guidelines to grade the “normality” of hearing ability even intechnical directives (1, 3). Moreover, it may also be of interest to investigate the soundscape perceptionof persons with varying degrees of age-related hearing loss (i.e. elderly). Therefore, there is a significant
impetus for an automated audiometric test that does not require expert knowledge or professional audiometricequipment for the purposes of screening ability before listening experiments.
Hearing ability is usually assessed by audiometric tests administered by a trained healthcare professional(e.g. audiologists, otolaryngologists). Since hearing impairment is a medical diagnosis, audiometric tests
are manually administered by qualified personnel and governed by internationally recognised guidelines (4–6), and conducted in strict acoustic conditions with calibrated audiometric equipment (7). Beyond medical
diagnoses, compliance with the stringent requirements of audiometric tests to assess hearing ability for listening experiments becomes prohibitively expensive and laborious. Nonetheless, a number of soundscapestudies with listening tests have reported the hearing ability of the recruited participants based on pure tone
audiometry (PTA) with professional audiometric equipment (8–13).
To overcome the barriers due to cost and availability of audiometric tests and qualified healthcare personnel, mobile health technologies have been developed and deployed, especially in the form of smartphonebased apps for preventive screening in schools and for the elderly (14, 15). A recent meta-analysis of
1 {ztong, bhanlam, wooi002, karn001, trevor.wong, ewsgan} @ ntu.edu.sg* These authors contributed equally to this workFigure 1: Procedure of pure tone audiometric test on uHear app
smartphone-based audiometry reported a respectable aggregated diagnostic sensitivity of 89 % [(83 %, 93 %),95 % CI] and specificity of 93 % [(87 %, 97 %); 95 % CI] for PTA (16). Few apps have been scientifically validated, with the rarity of the iOS-based uHear app (uHear, Unitron – Sonova Holding AG, Stäfa, Switzerland)
subjected to some rigor (17–20). Moreover, it was found that device output levels were similar across mobile
phone models running the iOS platform (Apple Inc., California, USA) (21), and its bundled headphones were
found to be more accurate than supra- or circumaural headphones (20), thereby increasing the availabilityof any iOS-based audiometry apps. To the authors’ best knowledge, there are also no published soundscapeliterature that screened for hearing ability using smartphone-based applications.
To this end, there is an absence of studies examining the efficacy of smartphone-based audiometric apps
to primarily screen for normal hearing. Based on reported literature, this study thus examines the efficacy of
the iOS-based uHear app in comparison with a “gold standard” audiometer in detecting normal hearing basedon the latest World Health Organisation (WHO) grading scheme (22).

2.

METHODOLOGY

2.1 ParticipantsThis study was conducted as an auxiliary to two ongoing subjective listening experiments at Nanyang
Technological University (NTU), Singapore. A total of 163 participants were recruited after first screening forany self-reported hearing loss, of which there were none. Participants were approximately evenly distributedin gender [female: 91 (55.8 %); male: 72 (44.2 %)] with a relatively young age distribution between 18 and75 years (x̄age = 27.3 years, SDage = 11.4, SEage = 0.89). Formal ethical approvals were obtained from theInstitutional Review Board (IRB) of NTU (Ref. IRB-2020-08-035 and IRB-2021-293) for the responses
collected.
2.2 Pure Tone Audiometry
Conventional pure tone audiometry (PTA) was conducted using a “gold standard” audiometer (AD629, Interacoustics A/S, Middelfart, Denmark) in a quiet room within the permissible ambient noise limits (6). Conventional PTA is a manual determination of threshold levels as defined by the American Speech-LanguageHearing Association (4), and is also referred to as the ascending method in ISO 8253-1, or the modified
Hughson-Westlake procedure as described in the British Society of Audiology guidelines (5, 6). It is characterized by its “5 dB up” and “10 dB down” procedure in determining threshold levels. In contrast, theuHear app is an automated implementation of the modified Hughson-Westlake method with the same startinglevel of 30 dB HL (Hearing Level) as the conventional PTA. All the thresholds determined by the uHear app
were through the same set of devices (iPhone 4S and Earpods, Apple Inc., California, USA). Instructions to
conduct the automated audiometric assessment on the uHear app was presented via instructions on a graphic
user interface, as shown in Figure 1.

2.3 Validation procedure
The audiograms obtained from both the audiometer and uHear app were graded based on the WHO hearing loss grading system (22). The grading criteria for “normal hearing” requires the hearing threshold (HT)in the better ear to be less than 20 dBHL (lowered from 26 dBHL in 2021), and that the HT in the worse ear isless than 35 dBHL. The HT is determined by the arithmetic mean of hearing levels in dB HL across 4 tested
frequencies (0.5, 1, 2, and 4 kHz). Since the uHear app also assesses the 6 kHz frequency, it is included alongwith the 4 frequencies in the WHO grading scheme, denoted as “WHO+”. The suitability of the uHear app
would be assessed based on a set of metrics (i.e. accuracy, sensitivity, specificity, and precision) in grading
normal hearing via both WHO and WHO+ schemes.
The accuracy, precision, sensitivity and specificity of the uHear app in determining “normal hearing”
was computed based on the binary confusion matrix with the audiometer as the reference. Positives (“1”)
represent the grading of “normal hearing”, whereas negatives (“0”) indicate otherwise. The overall accuracy
is determined by
Accuracy = (TP + TN)/K,(1)
where TP and TN are “true positives” and “true negatives”, and K is the total number of positives and negatives. The sensitivity and specificity are determined by
Sensitivity = TP/(TP + FN),

(2)

Specificity = TN/(FP + TN),

(3)

and
respectively. Lastly, the precision is given by
Precision = TP/(TP + FP).

(4)

2.4 Data analysis
Due to the interval nature of the audiogram measurements, non-parametric representations of the BlandAltman plots were presented to investigate the agreement between both methods (23, 24). Distribution-freetests and confidence intervals (CI) were computed for the median to examine the bias, and also for the
2.5th and 97.5th percentiles to discover outliers (25). The accuracy, sensitivity, specificity, and precisionof the uHear app in grading normal hearing ability was evaluated in comparison to the audiometer. To
determine if there were differences between both methods in grading normal hearing, a McNemar’s test wasalso conducted (26). Finally, the non-parametric Wilcoxon signed rank tests were employed to determine thedifferences between the methods for each test frequency (27). All data analyses were conducted with the R
programming language (27) on a 64-bit ARM environment. The data that support the findings of this study
are openly available in NTU research data repository DR-NTU (Data) at doi:10.21979/N9/JQDI6F, and thereplication code is available on GitHub at github.com/ntudsp/douHear.

3.

RESULTS AND DISCUSSION

3.1 Agreement between the uHear app and the audiometerIn the non-parametric implementation of the Bland-Altman plot, the median of the paired differences between audiometer and uHear measurements is used to judge the measurement bias. The lower and upper
limits of agreement (LoA) are represented by the 2.5th and 97.5th percentiles, respectively. Since no differences were lower or higher than the 95 % confidence intervals of the lower or upper tails of the respective
lower and upper LoAs, no outliers were detected at each frequency across both ears, as shown in Figure 2.
Moreover, the measurements appear to be symmetrically distributed about the medians.
A slight overall bias of about −5 dB [(−5, −5), 95 % CI] in the uHear app is reflected in the medianacross both ears when considering differences from all frequencies. The overall bias could be attributed tothe −5 dB bias at 1 kHz [(−5, −5), 95 % CI] in the left ear; and −5 dB bias at 1 [(−10, −5), 95 % CI], 2[(−5, −5), 95 % CI] and 4 kHz [(−10, −5), 95 % CI] frequencies in the right ear, since the equality lines
were beyond the confidence intervals in those cases. Hence, there is a greater tendency to obtain a better
hearing threshold using the uHear app. This leniency could lead to an increase in false positives.

0.5

1

2

40
20

97.5th : 10

Difference between PTA and uHear (dB HL)

−20

M=0

M = −52.5th : − 15

2.5th : − 15

2.5th : − 15

97.5th : 5

97.5th : 5

M = −5M = −52.5th : − 15

2.5th : − 15

Left

0

97.5th : 10

97.5th : 5

M=0

−40
40
20

M=0

0

−20

2.5th : − 15

Right

97.5th : 9.75

−40
0

20

40

60

0

20

4

40

60

0

20

6

40

60

All

40
97.5th : 20
97.5th : 100

M = −597.5th : 15

M=0

M = −5

2.5 : − 10

Left

20

th

−20

2.5 : − 15th

2.5th : − 15−40
40
97.5th : 20

20

97.5th : 150

M = −5−20

2.5th : − 15

M=0

M = −5Right

97.5 : 5th

2.5th : − 15

2.5th : − 15

−40
0

20

40

60

0

20

40

60

0

20

40

60

Arithmetic average between PTA and uHear (dB HL)

Figure 2: Bland-Altman plot of agreement between the pure tone audiometry and uHear mobile app formeasuring hearing level (dB HL) across 0.5, 1, 2, 4, 6 kHz frequencies for both ears. The average bias
is estimated by the median of the differences and limits of agreement by the 2.5th and 97.5th percentiles.
Distribution-free confidence intervals are based on 95 % confidence level for the upper and lower percentiles(■) and the median (■).
3.2 Normal hearing grading performance of uHear
The normal hearing grading outcomes of all 163 participants across both WHO and WHO+ schemes arepresented as confusion matrices, as shown in Figure 3. At a glance, the number of TNs and FPs were about
the same across both schemes, but the number of FPs were substantially lower in the WHO+ scheme. The
computed performance metrics outlined in Section 2.3 for the uHear app using both WHO and WHO+ grading
schemes are summarized in Table 1. Across the metrics, the WHO+ scheme appears to be outperforming the
WHO scheme, especially in terms of specificity. The low specificity across both schemes provides further
evidence of the inability of the uHear app in correctly grading the presence of hearing loss (i.e. TN). Using
the McNemar’s test (MNT), however, the differences in sensitivity and specificity between the uHear app andthe audiometer was not statistically significant in either schemes (p > 0.05), as shown in Table 1.
Different from audiometric diagnostics that detect instances and assess the severity of hearing loss, theaim of the audiometric test for listening experiments is to screen for normal hearing as an inclusion criteria.
Whereas FPs would compromise the quality of listening experiments that require participants with normal
hearing, it is not detrimental to the listening experiment when an FN occurs (apart from losing potential
participants). Hence, for the purpose of screening for normal hearing, precision would be the most important
metric to determine the suitability of the uHear app as a low-cost replacement for audiometers, as seen from
Equation (4). Although both WHO and WHO+ grading schemes are comparable in terms of precision, it isworth noting that there was also an overall change in the reference audiometer grading in the WHO (normal:
155 (95.1 %); not normal: 8) and WHO+ (normal: 152; not normal: 11) schemes.
3.3 Audiometric differences between uHear and the audiometer
To investigate the differences between the audiometer and uHear audiograms at each frequency, the nonparametric Wilcoxon signed rank test (WSRT) was conducted. The choice of the non-parametric WSRT

151

2
18.2%

5

5

1

9

3.2%

62.5%

10%

99.3%

0

37.5%

50%

96.8%

0.7%

1.3%

3

90%

150

98.7%

0

1

1

2%

0

98%

1

50%

1
0

uHear

Audiometer

81.8%

Figure 3: Confusion matrix of the uHear app against the audiometer in grading normal hearing. Normal
hearing is indicated as “1”, and “0” otherwise, wherein “11”, “01”, “00”, and “10”, respectively indicates“true positive” (TP), “false negative” (FN), “true negative” (TN), and “false positive” (FP). Grading is basedon the hearing threshold (HT) calculations from WHO (left) and WHO+ (right) schemes, where the latter
uses HTs based on the arithmetic mean across all frequencies measured in the uHear app.
Table 1: Accuracy, sensitivity, specificity, and precision, and McNemar’s Test p-value of the WHO andWHO+ grading criteria for normal hearing. WHO+ includes the measurements at 6 kHz in the grading,which is on top of the 4 frequencies in the WHO grading (i.e. 0.5, 1, 2, and 4 kHz).
Grading

Accuracy (%; 95 % CI)

Sensitivity (%)

Specificity (%)

Precision (%)

MNT p-value

WHO
WHO+

95.09 (90.56, 97.86)
98.16 (94.72, 99.62)

96.77
99.34

62.50
81.82

98.04
98.69

0.723
1.000

was motivated by the interval nature of the measurements, as well as the symmetry in the measurements asshown in Figure 2. The difference in audiograms between the audiometer and the uHear app was found to
be statistically significant at all frequencies across both ears at 5 % significance level, except at 6 kHz for theright ear, as shown in Table 2.
The computed pseudomedian (28) of the differences between the audiometer and uHear measurements
depicts a clear negative bias in the uHear measurements from 0.5 up to 4 kHz in both ears. Given the narrowconfidence intervals, the pseudomedians were almost equivalent to the computed median of the differencesin the Bland-Altman plots in Section 3.1, which confirms the symmetry of the measurements (i.e. highprecision). Unsurprisingly, the strongest evidence of difference occurred at the 1 and 4 kHz tones, as also
shown in Figure 2. The distinct differences also corroborates with previous investigations on the uHear andother mobile application based assessment systems (20).
Interestingly, the pseudomedians at 6 kHz in both ears were near zero and the WSRT was either weakly
significant (Left ear; 0.01 < p = 1.13 × 10−3 < 0.05) or not significant (Right ear; p = 6.82 × 10−2 > 0.05).
Hence, the increase in precision with the addition of the 6 kHz measurement in the grading for normal hearing
could be attributed to the similarity of the uHear app to the audiometer at the 6 kHz test tone. The uHear app
was also found to match audiometers more closely in the mid to high frequencies (≥2 kHz) in previous studies
(20).

4.

CONCLUSION

A mobile-based application (uHear) was assessed for its suitability as a screening tool for normal hearing
in participants of listening experiments. Due to the availability of a test frequency (i.e. 6 kHz) beyond thoserequired by the WHO guidelines to grade normal hearing abilities, two grading schemes, namely the WHO
and the modified WHO+, were investigated. Based on the assessment with 163 participants, the uHear app

Table 2: Wilcoxon signed rank tests, and the pseudomedian and its confidence intervals (dB HL; 95% confidence level) of the differences between the audiometer and uHear audiograms are computed at each frequency(0.5, 1, 2, 4 and 6 kHz) across both ears. The null hypothesis is rejected at p-value < 0.05.
Ear

Frequency (kHz)

PseudomedianConfidence Interval (95 %)p-value

Left

0.5
1
2
4
6

−5.0
−7.5
−5.0
−5.0
2.5

( −5 , −5 )( −7.5, −7.5)( −7.5, −2.5)( −7.5, −5 )( 0 , 5 )6.11 × 10−7
3.37 × 10−19
7.14 × 10−7
1.65 × 10−10
1.13 × 10−3

Right

0.5
1
2
4
6

−5.0
−7.5
−5.0
−7.5
2.5

( −5 , −2.5)( −7.5, −7.5)( −7.5, −5 )( −7.5, −7.5)( 0 , 2.5)

2.14 × 10−6
4.31 × 10−23
1.61 × 10−13
1.67 × 10−16
6.82 × 10−2

was comparable with the audiometer in precision – the key metric in screening for normal hearing for listening
tests – using both grading schemes. Despite higher overall accuracy, sensitivity, and precision, the proposed
WHO+ grading scheme should only be used after assessing the need to include the 6 kHz test frequency basedon the screening requirements.
Nevertheless, the uHear app still falls short as a low-cost diagnostic tool for assessing the severity ofhearing impairment due to significant deviation from the audiometer across frequencies. Further caution
should be exercised due to the low specificity (i.e. ability in detecting instances of hearing impairment). The
results presented were also obtained via a singular device with a now deprecated audio interface (i.e. 3.5 mm)
and should be examined for variations in newer models and modern audio interfaces in the future.

ACKNOWLEDGEMENTS
This research is supported by the Singapore Ministry of National Development and the National Research
Foundation, Prime Minister’s Office under the Cities of Tomorrow Research Programme (Award No. COTV4-2020-1). Any opinions, findings and conclusions or recommendations expressed in this material arethose of the authors and do not reflect the view of National Research Foundation, Singapore, and Ministry of
National Development, Singapore.

REFERENCES
1.

2.

3.

4.
5.
6.
7.

8.

International Organization for Standardization. ISO/TS 12913-2: Acoustics — Soundscape — Part
2: Data collection and reporting requirements. Geneva, Switzerland: International Organization forStandardization; 2018.
International Organization for Standardization. ISO 532-1:2017 - Acoustics — Method for calculating
loudness — Part 1: Zwicker method. vol. 7. Geneva: International Organization for Standardization;
2017.
International Telecommunication Union Radiocommunication Sector.
Recommendation ITU-R
BS.1116-3: Methods for the subjective assessment of small impairments in audio systems. Geneva,
Switzerland: International Telecommunication Union; 2015.
American Speech-Language-Hearing Association. Guidelines for Manual Pure-Tone Threshold Audiometry; 2005. Available from: http://www.asha.org/policy/GL2005-00014/.
British Society of Audiology. Pure-tone air-conduction and boneconduction threshold audiometry withand without masking. Bathgate, UK: British Society of Audiology; 2018.
International Organization for Standardization. Acoustics — Audiometric test methods - Part 1 : Puretone air and bone conduction (ISO 8253-1:2010). International Organization for Standardization; 2010.
International Organization for Standardization. Acoustics — Reference zero for the calibration of audiometric equipment - Part 1: Reference equivalent threshold sound pressure levels for pure tones andsupra-aural earphones (ISO 389-1:2017). International Organization for Standardization; 2018.
Hong JY, Lam B, Ong ZT, Ooi K, Gan WS, Kang J, et al. Effects of contexts in urban residential areas on

the pleasantness and appropriateness of natural sounds. Sustainable Cities and Society. 2020 12;63(PG-):102475.
9. Medvedev O, Shepherd D, Hautus MJ. The restorative potential of soundscapes: A physiological investigation. Applied Acoustics. 2015;96:20-6. Available from: http://dx.doi.org/10.1016/j.apacoust.2015.
03.004.
10. Hao Y, Kang J, Wörtche H. Assessment of the masking effects of birdsong on the road traffic noise
environment. The Journal of the Acoustical Society of America. 2016 8;140(2):978-87.
11. Lee SC, Hong JY, Jeon JY. Effects of acoustic characteristics of combined construction noise on annoyance. Building and Environment. 2015 10;92:657-67.
12. Rådsten Ekman M, Lundén P, Nilsson ME. Similarity and pleasantness assessments of waterfountain sounds recorded in urban public spaces. The Journal of the Acoustical Society of America.
2015;138(5):3043-52.
13. Axelsson Ö, Nilsson ME, Berglund B. A principal components model of soundscape perception. The
Journal of the Acoustical Society of America. 2010 11;128(5):2836-46. Available from: http://asa.
scitation.org/doi/10.1121/1.3493436.
14. World Health Organization. Hearing screening: considerations for implementation. Geneva, Switzerland: World Health Organization; 2021.
15. Manus M, van der Linde J, Kuper H, Olinger R, Swanepoel DW. Community-Based Hearing and Vision Screening in Schools in Low-Income Communities Using Mobile Health Technologies. Language,
Speech, and Hearing Services in Schools. 2021 4;52(2):568-80.
16. Chen CH, Lin HYH, Wang MC, Chu YC, Chang CY, Huang CY, et al. Diagnostic Accuracy of
Smartphone-Based Audiometry for Hearing Loss Detection: Meta-analysis. JMIR mHealth and uHealth.
2021 9;9(9):e28378.
17. Saliba J, Al-Reefi M, Carriere JS, Verma N, Provencal C, Rappaport JM. Accuracy of Mobile-Based
Audiometry in the Evaluation of Hearing Loss in Quiet and Noisy Environments. Otolaryngology–Headand Neck Surgery. 2017 4;156(4):706-11.
18. Li LYJ, Wang SY, Wu CJ, Tsai CY, Wu TF, Lin YS. Screening for Hearing Impairment in Older Adults by
Smartphone-Based Audiometry, Self-Perception, HHIE Screening Questionnaire, and Free-Field Voice
Test: Comparative Evaluation of the Screening Accuracy With Standard Pure-Tone Audiometry. JMIR
mHealth and uHealth. 2020 10;8(10):e17213.
19. Melo IMM, Silva ARX, Camargo R, Cavalcanti HG, Ferrari DV, Taveira KVM, et al. Accuracy ofsmartphone-based hearing screening tests: a systematic review. CoDAS. 2022;34(3).
20. Barczik J, Serpanos YC. Accuracy of Smartphone Self-Hearing Test Applications Across Frequencies
and Earphone Styles in Adults. American Journal of Audiology. 2018 12;27(4):570-80.
21. Patel K, Thibodeau L, McCullough D, Freeman E, Panahi I. Development and Pilot Testing of
Smartphone-Based Hearing Test Application. International Journal of Environmental Research and Public Health. 2021 5;18(11):5529.
22. World Health Organisation. World Report On Hearing. Geneva: World Health Organisation; 2021.
Available from: https://www.who.int/publications/i/item/world-report-on-hearing.
23. Martin Bland J, Altman D. Statistical methods for assessing agreement between two methods of clinicalmeasurement. The Lancet. 1986 2;327(8476):307-10. Available from: https://linkinghub.elsevier.com/
retrieve/pii/S0140673686908378.
24. Bland JM, Altman DG. Measuring agreement in method comparison studies. Statistical Methods in
Medical Research. 1999 4;8(2):135-60.
25. Fay MP. asht: Applied Statistical Hypothesis Tests; 2022. Available from: https://cran.r-project.org/
package=asht.
26. Kuhn M. caret: Classification and Regression Training; 2019. Available from: https://github.com/topepo/
caret/.
27. R Core Team. R: A language and environment for statistical computing. R Foundation for StatisticalComputing. Vienna, Austria: The R Foundation for Statistical Computing; 2021. Available from: https://www.r-project.org/.
28. Hollander M, A Wolfe D, Chicken E. Nonparametric Statistical Methods. vol. 17 of Wiley Series in
Probability and Statistics. Wiley; 2015.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0017

Differences in thermal-acoustic perception in various officebehaviors
Xin WEN1; Qi MENG2*; Jian KANG3; Yue WU4
1School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural

Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology,
China
2

School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural

Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology,
China
3

UCL Institute for Environmental Design and Engineering, University College London (UCL), UK

ABSTRACT
Thermal-acoustic interaction has vital research value in the development of soundscape prediction models.
Previous studies on thermal–acoustic interaction have been focused mainly on perceptual changes.
However, the differences in office behaviors warrant attention. We conducted an experimental study to
explore the effects of various office behaviors (such as resting, reading, writing, and typing) on thethermal–acoustic interactive perception. The results showed that (at near thermal neutral temperature) (1)sound types affected thermal evaluation, acoustic evaluation, and overall evaluation. The sound of watersignificantly reduced the score for thermal sensation. (2) Behavior types affected thermal sensation,
acoustic comfort, and overall comfort. Reading contributed to significantly lower scores than otherbehaviors for the three indicators. This indicated that when reading, people are more demanding of theenvironment. (3) The interaction of sound types and behavior types affected overall annoyance. Therefore,
we recommend adjusting the office environment effectively and establishing more effective soundscapeprediction models.
Keywords: Office behaviors, Thermal-acoustic interaction, Perception

1. INTRODUCTION

Office behaviors play an important role in the lives of people who spend time in an office every
day; thus, a good office environment is crucial. Researchers have conducted numerous studies on
office comfort, such as the thermal, acoustic, and visual comfort of the indoor environment (1-9). Ina study of multiple environmental factors, it was found that temperature and noise have a veto power
on indoor satisfaction (1), so the thermal and acoustic environments of the office space areparticularly important. Several scholars have studied the interactive effects of thermal and acoustic
environments on human comfort (2, 3, 6-9).
Fanger studied individuals’ thermal sensation with 40 dBA background noise and 85 dBA white
noise at a room temperature of 25 °C and found that sound had no effect on thermal neutral
temperature (6). Tiller et al. found that noise affected thermal comfort, while temperature did notaffect acoustic perception (PMV -1, -0.5, 0, 0.5 and 1)(7). Nagano and Horikoshi investigated theinteraction between temperature and noise under high and low temperature conditions and found that
1
2738333667@qq.com
mengq@hit.edu.cn(corresponding author)

3

j.kang@ucl.ac.uk

4wuyuehit@hit.edu.cn

noise did not affect thermal sensation but did affect thermal comfort, and temperature affected
acoustic sensation (8, 9). Yang and Moon found that noise had no effect on thermal sensation, but
noise level and type had significant effects on thermal comfort, and thermal environment had noeffect on subjective loudness and noisiness, but the effect of PMV (PMV -1.53, 0.03, 1.53 and 1.83)
on acoustic comfort and annoyance was statistically significant (2). Guan et al. found that acoustic
conditions affected thermal comfort but not thermal sensation, and acoustic comfort was not affectedby temperature in the near thermal neutral zone. Their laboratory experiments were based on the
PMV equation, setting room temperatures at 18, 22, and 26 °C, corresponding to slightly cold,
neutral, and slightly warm sensations, respectively (3). They also found that total comfort was
mainly affected by temperature and sound pressure level (SPL), as well as by temperature* SPLinteraction (3).
The concept of comfort has been used in studies of indoor environmental quality, mainly toinvestigate physical/physiological sensations and the perception of physical factors such as acoustics,
thermal conditions, lighting, and indoor air quality (1, 4, 5). However, most studies did not restrict
human behavioral states, and subjects were allowed to read or rest and practice other low metabolic
rate office behaviors (1-5).
In terms of thermal environment research, metabolic rate has been investigated as a researchindex and found to be different for various office behaviors (10-14). ASHRAE 55 establishedmetabolic rates for different office activities: sitting and resting (1.0 met), reading (1.0 met), writing(1.0 met), typing (1.1 met), sitting and filing (1.2 met), standing and filing (1.4 met), walking about(1.7 met), and lifting or pacing (2.1 met). Additionally, the researchers found that different officebehaviors resulted in different perceptions (15). Pronk found that 34 participants felt significantly
less discomfort from prolonged fixed sitting postures and more comfortable at work after a 5-week
sit–stand office experience (16). Yang et al. found that different activity levels (metabolic rates) had
significant effects on human thermal sensation, whereby differences in thermal comfort were found(17).
Because of the diversity of office worker behaviors, the original study was not able to predict the
comfort of people practicing different behaviors. Therefore, it is necessary to study the effects ofdifferent office behaviors on thermal-acoustic perception. The purpose of this study is to investigate
the variability of thermal-acoustic perception under different sound source conditions and behavioral
states. The combined effects of acoustic and behavioral states on thermal sensation, thermal comfort,
subjective loudness, acoustic comfort, overall comfort, and overall annoyance are investigatedthrough laboratory experiments.

2. METHODS
2.1 Experimental Chamber
The experiment was conducted in the indoor climate chamber (L*W*H:4200*4200*3100 mm) of
the Harbin Institute of Technology (HIT), furnished as a small office. The air temperature, relative
humidity (RH), and air velocity were controlled by independent air-conditioning systems. The indoor
temperature was set at 0-40 ± 0.1 ℃ and relative humidity at 20-90 ± 3%, which met therequirements for the experiment. The airflow started from the ceiling louvers and returned via the air
inlet at the lower part of the sidewall. Sound stimuli were played through headphones to avoid theinfluence of room reverberation on the experiment.
2.2 Participants
After considering the level of statistical power, effect size (1- β = 0.8, α = 0.05, effect size =0.25), and the experimental conditions, 40 university students (17 males and 23 females) wererecruited for the experiment, and each participated in a range of 1-3 experimental phases. The
experiment was divided into three phases: 20, 23, and 26 °C, with sample sizes of 30 (15 males and
15 females), 30 (16 males and 16 females), and 30 (15 males and 15 females), respectively. On the
day of the experiment, all subjects were graduate students who reported normal hearing and nophysical discomfort such as fever or cold. The details of the participants are shown in Table 1.
According to ASHRAE 55 standards (15), participants were advised to wear a clothing ensemble ofapproximately 0.96 clo, usually consisting of a pair of trousers, a long-sleeved shirt, a suit jacket,
socks, underwear, and sneakers, for a total heat resistance of approximately 1.1 clo, including thechair.

Table 1 – Details of participants
Mean (S.D.)

Max

Min

Age

24.16 (1.62)

28

22

Height (mm)170 (7.21)

183

155

Weight (Kg)

60.17 (10.52)

95

44

BMI

20.55 (2.50)

28

17

2.3 Experimental Conditions
According to the PMV equation, three room temperature levels (20, 23, and 26 °C) were selected,
corresponding to slightly cold, neutral, and slightly hot sensations, respectively. As people perform
various activities, their energy metabolic rate increases at the level of the basal metabolism. The
metabolic rate has an impact on thermal evaluation, and the body's neutral temperature decreases tomaintain the body's homeostasis when the metabolic rate increases (18-20). In a quiet state, thehuman metabolism is more stable in an environment at 20-30 °C. Previously, Yang et al.
demonstrated that there was no significant difference in metabolic rate between sitting and typing
work at ambient temperatures of 20, 23, and 26 °C (17). For each temperature level, a constantrelative humidity of 45% was set and the wind speed was less than 0.1 m/s. To ensure equivalentroom air temperature and mean radiant temperature, each thermal condition was set at least 15 h
before the test. This room was an indoor room. The average radiant temperature was assumed to beequal to the air temperature. The metabolic rate was approximately 1.0 met (1.0met¼58.2W/m2) andthe clothing insulation was 1 clo (1clo¼0.155m2K/W).
Five sound sources (traffic noise, air-conditioning noise, music, speech, and water) were selected
as sound samples because they represented typical sound sources in the office environment (3, 4). In
this paper, traffic noise refers to the noise of the main road (vehicles with an average speed of 50km/h). Air-conditioning noise refers to the noise generated by air conditioners (dimensions: 0.5
m*1.75 m*0.335 m; mechanical noise levels: 22-46 dB (internal), 42-56 dB (external) recorded in
the anechoic chamber). The music was “Souvenir d'enfance,” calm music with no apparent emotional
value, downloaded from the music material library (21). The voice was a Chinese recording
introducing architectural design. The water was recorded in a park in Harbin on a sunny, windlessafternoon with the sound of rushing water. To avoid the effect of SPL changes on the results, we used
Adobe Audition software to adjust the SPL of the six sound samples to 65 dBA. The SPL was chosenwith reference to the median daytime noise exposure level measured through loudspeakers (22).
Meanwhile, 65 dBA has been used as an intermediate value several times in previous
thermal–acoustic interaction studies (3, 4).
Four work behaviors with lower metabolic rates were selected (resting, reading, writing, andtyping), according to the ASHRAE 55-2020 standard that established typical activities in the officeenvironment, including sitting and resting (1.0 met), reading (1.0 met), writing (1.0 met), and typing(1.1 met), etc. Meanwhile, Yang et al. demonstrated no significant difference in metabolic rate forsitting and typing work at ambient temperatures of 20, 23, and 26 °C. Since the participants were all
graduate students in architecture, the materials for the reading task were books on architecture, andthe writing task was a summary transcription of the contents of architecture books (17). The
materials for the typing task were provided by Kingsoft Typing 2013, a typing practice software
launched by Kingsoft.
2.4 Experimental Design and Procedure
A between-subjects factorial design was used in this study. The experiment included 3
independent variables: room temperature (20, 23, and 26 °C), type of sound source (traffic noise,
air-conditioning noise, music, speech, and water sounds), and office behavior (resting, reading,writing, and typing). Subjects were randomly divided into 4 groups, and they participated in almost
all experiments. Four people in each group participated in the experiment at the same time, where
they experienced five types of acoustic environments and performed four different office behaviors
at the same temperature.
All experiments were conducted at a room temperature of 23 °C at the beginning, then at 20 °C
and finally at 26 °C. The clothing insulation of each subject was visually inspected before the start ofeach experiment to ensure it did not deviate significantly from the 0.96 clo requirement (4). Fourparticipants (maximum size of four per group) were provided with a demonstration of how tocomplete the work task and fill out the questionnaire. To avoid potential experimental bias, theexperimental conditions were not communicated to the participants. The participants then enteredand remained in the room for 30 minutes to acclimatize to the thermal environment (23, 24).
According to previous studies, the psychological response reached a steady state within 30 minutes(17), so subjects performed the corresponding office behavioral task during the 30 minutes. After theadaptation, the subject continued to perform the corresponding office behavior, and then the soundwas played for 1.5 minutes. After the sound stopped, the subject filled in the questionnaire and thetest ended. Subjects rested for 1 minute and then began the test for the next condition (see Figure 1).
Subjects completed four office behavior tasks at the same temperature during the day. Thermal
sensation (TSV), thermal comfort (TCV), subjective loudness (SLV), acoustic comfort (ACV),
overall comfort (OCV), and overall annoyance (OAV) were assessed using a 7-point Likert scale atthe end of each acoustic environmental condition period.

Figure 1 – Experimental procedure for each session
A factorial analysis of variance (ANOVA) was used to fit the 6 subjective environmental comfort
attributes of the experiment to the 2 independent variables of type of sound source and type of
behavior. ANOVA is a powerful statistical test and was used in this case, although the normality ofthe subjective scores could not be guaranteed (25). The Bonferroni post hoc test was applied.

3. RESULTS
3.1 Effects of Sound Source under Different Behaviors on Thermal Evaluation
The effects of sound types and behavior types on the thermal evaluation included the effects on
TSV and TCV. Figure 2 shows the average TSV values under different acoustic environments andbehavior types. The comparison of different sound types indicated that there were differences in TSV.
Traffic noise and air-conditioning noise contributed to higher TSV than music, speech, and water.
TSV for water was the lowest. Meanwhile, there were differences in TSV values under different
behaviors. Reading contributed the lowest value of TSV under any kind of sound. When playing
traffic noise, air-conditioning noise, and music, TSV for typing was highest. However, writing
contributed to higher TSV than the other behavior types while playing speech and water. Figure 3
shows the average TCV values under different acoustic environments and behavior types. There weredifferences in the TCV under different sound types. Music and water contributed to higher TSV thanspeech, traffic noise, and air-conditioning noise. The effects of behaviors on TCV were not obvious.
In most cases, TCV for reading was lower than for the other behaviors, and for typing it was higher.

Figure 2 – The TSV of different conditions
Figure 3 – The TCV of different conditions
Table 2 shows the significance of the indicators (TSV and TCV) under the main effects andinteraction. The ANOVA of TSV revealed that the main effects of both sound types and behavior

types on TSV were highly significant (p < 0.01), but the interactions had no effect on TSV (p > 0.05).
Regarding TCV, only the main effect of sound types was significant (p < 0.01).
Table 2 – The results of two-way ANOVA for TSV and TCV
Subjective evaluation
TSV

TCV

Sound type

Behavior type

Sound type *Behavior type

Df

4

3

12

F

7.594

4.000

0.218

Sig.

0.000

0.008

0.992

Df

4

3

12

F

7.234

2.329

0.234

Sig.

0.000

0.073

0.997

Table 3 lists the means and Bonferroni's post hoc test results to supplement Table 2. There wereeffects of sound source type on TSV. Water contributed to lower scores than the others withstatistical significance. For the effect of behavior types on TSV, the scores were statisticallysignificantly lower when reading and higher when typing. In terms of the effects of sound types on
TCV, the scores were significantly higher when playing water and music, and significantly lower
under air-conditioning noise. There was no significant difference in TCV for different behaviors.
Table 3 – Means and Bonferroni's post-hoc test results for TSV and TCV(Means with different letters are significantly different. P < 0.05, A > B > C > D > E)N

Thermal Sensation

Thermal Comfort

Mean

Group

Mean

Group

Sound

Traffic noise

372

4.5968A

4.5054BC

type

Air-conditioning372

4.5591A

4.3710

C

Music

373

4.4263A

4.7507A

Speech373

4.3995A

4.5791

AB

Water

372

4.1747B

4.7339A

Behavior

Rest

473

4.3923AB

type

Reading469

4.3044B

Writing455

4.4791AB

Typing465

4.5527A

noise

3.2 Effects of Sound Source under Different Behaviors on Acoustic Evaluation
The effects of sound types and behavior types on the acoustic evaluation included the effects on
SLV and ACV. Figure 4 shows the average SLV values under different acoustic environment andbehavior types. There were differences in SLV under different sound types. Music and water
contributed to higher TSV than speech, traffic noise, and air-conditioning noise. The effects of
behaviors on SLV were not obvious. In most cases, SLV was lower when reading and higher whenwriting and typing. Figure 5 shows the average ACV values under different acoustic environments
and behavior types. The comparison of different sound types indicated that there were differences in
the ACV. Water and music contributed to higher ACV than speech, traffic noise, and air-conditioning
noise. Meanwhile, there were differences in ACV under different behaviors. In most cases, reading
accounted for the lowest ACV value, except for traffic noise.

Figure 4 – The SLV of different conditions
Figure 5 – The ACV of different conditions
Table 4 shows the significance of the indicators (SLV and ACV) under the main effects andinteraction. The ANOVA of SLV revealed that only the main effect of sound types on SLV wassignificant (p < 0.01), but the main effect of behavior types and the interactions had no effect on SLV(p > 0.05). In terms of ACV, the main effects of both sound types and behavior types were highly
significant (p < 0.01), but the interaction had no effect on ACV (p > 0.05).
Table 4 – The results of two-way ANOVA for SLV and ACV
Subjective evaluation
SLV

ACV

Sound type

Behavior type

Sound type *Behavior type

Df

4

3

12

F

129.002

2.026

1.008

Sig.

0.000

0.108

0.439

Df

4

3

12

F

509.476

6.163

1.349

Sig.

0.000

0.000

0.184

Table 5 lists the means and Bonferroni's post hoc test results to supplement Table 4. There wereeffects of sound source type on SLV. Water and music contributed to higher scores than speech,
air-conditioning noise, and traffic noise with statistical significance. For the effect of behavior types
on SLV, the scores did not differ significantly. There were also effects of sound source type on ACV.
ACV was significantly higher when playing water and music than speech, air-conditioning noise,and traffic noise with statistical significance. For the effect of behavior types on ACV, the scores
were statistically significantly lower when reading.
Table 5 – Means and Bonferroni's post-hoc test results for SLV and ACV(Means with different letters are significantly different. P < 0.05, A > B > C > D > E)N

Subjective Loudness

Acoustic Comfort

Mean

Group

Mean

Group

Sound

Traffic noise372

2.7661

D

3.000

C

type

Air-conditioning372

2.8199D

2.9247

C

Music

373

3.6783B

5.1528A

Speech373

3.1394C

3.6836

B

Water

372

4.1048A

5.2769A

Behavior

Rest

473

4.0810A

type

Reading469

3.8436B

noise

Writing

455

4.0791A

Typing465

4.0323A

3.3 Effects of Sound Source under Different Behaviors on Overall Evaluation
The effects of sound types and behavior types on the overall evaluation included the effects on
OCV and OAV. Figure 6 shows the average OCV values under different acoustic environment andbehavior types. The comparison of different sound types indicated that there were differences in the
OCV. Music and water contributed to higher OCV than speech, traffic noise, and air-conditioning
noise. Meanwhile, there were differences in OCV under different behaviors. In most cases, reading
accounted for the lowest OCV value, except for traffic noise. Figure 7 shows the average OAVvalues under different acoustic environments and behavior types. There were differences in OAV
under different sound types. Water and music contributed to higher OAV than speech, traffic noise,
and air-conditioning noise. In terms of the effects of behavior types on OAV, the scores for readingwere significantly lower than for other behaviors when playing water sounds, except traffic noise.
OAV was higher for resting, writing, and typing when playing air-conditioning noise and speech, andfor writing and resting when playing music and water. OAV for typing was highest when traffic noise
was played.

Figure 6 – The OCV of different conditions
Figure 7 – The OAV of different conditions
Table 6 shows the significance of the indicators (OCV and OAV) under main effects andinteraction. The ANOVA of OCV revealed that the main effects of both sound types and behavior
types on TSV were highly significant (p < 0.01), but the interactions had no effect on TSV (p > 0.05).
In terms of OAV, the main effect of the two and the interactions were all significant (p < 0.05).
Table 6 – The results of two-way ANOVA for OCV and OAVSubjective evaluation
OCV

OAV

Sound type

Behavior type

Sound type *Behavior type

Df

4

3

12

F

342.672

5.286

1.495

Sig.

0.000

0.001

0.119

Df

4

3

12

F

354.382

8.410

1.885

Sig.

0.000

0.000

0.032

Table 7 lists the means and Bonferroni's post hoc test results to supplement Table 6. There wereeffects of sound types on OCV. Water and music contributed to higher scores than speech, traffic
noise and air-condition noise with statistical significance. For the effect of behavior types on OCV,
the scores were statistically significantly lower when reading.
Table 7 – Means and Bonferroni's post-hoc test results for OCV(Means with different letters are significantly different. P < 0.05, A > B > C > D > E)

N
Sound type

Overall comfort
Mean

Group

Traffic noise372

3.3387C

Air-conditioning372

3.2231

C

Music

373

5.1528A

Speech373

3.8231

B

Water

372

5.2634

A

Rest

473

4.2260A

Reading469

3.9979B

Writing455

4.2110A

Typing465

4.2108A

noise

Behavior type

In terms of the effects of sound types on OAV, owing to the presence of interaction, only theeffects of the interaction on OAV were analyzed, and simple effects tests were performed. Table 8
lists the means and Bonferroni's post hoc test results to supplement Table 6. There were differencesin effects of behavior types on OAV when playing different sounds. When playing traffic noise andwater, behavior types had no significant effect on OAV, while the scores for reading were lower thanfor other behaviors under conditions of air-condition noise, music, and speech with statisticalsignificance. OAV for rest was significantly higher than other behaviors when speech was played,
but for air-conditioning noise and music, scores at rest were not significantly different from writing
and typing.
Table 8 – Means and Bonferroni's post-hoc test results for OAV(Means with different letters are significantly different. P < 0.05, A > B > C > D > E)
N

Overall AnnoyanceRest

Reading

WritingTypingSound

Traffic noise372

3.258 A

3.200 A

3.198 A

3.430 A

type*

Air-conditioning372

3.255 AB2.989 B

3.286 A

3.323 A

Behavior

noise

type

Music

373

5.255 AB4.968 B

5.319 A

5.043 AB

Speech373

4.074 A

3.305 C3.714 B

3.763 B

Water

372

5.298 A

5.117 A

5.330 A

5.108 A

4. CONCLUSION
In this study, the effects of sound types and behavior types on thermal evaluation, acoustic
evaluation, and overall evaluation were investigated to measure thermal–acoustic perception under
different acoustic environments and behaviors.
First, the sound types affected the thermal evaluation, acoustic evaluation, and overall evaluation.
In terms of TSV, the sound of water reduced significantly the score. In the other subjective
evaluation indexes, the effect of sound source type was consistent. The scores for music and water
were higher than for speech; the scores for traffic noise and air-conditioning noise were the lowest.
This suggested that people show consistency in different indicators of thermal–acoustic perception
for their sound type preferences.

Second, the behavior types affected TSV, ACV, and OCV. In terms of TSV, the scores for readingwere the lowest and for typing were the highest. In terms of ACV and OCV, reading contributed
more to the significantly lower scores than other behaviors. This indicated that when reading,
subjective perception scores are lower and people are more demanding of the environment.
Finally, the interaction of sound types and behavior types affected overall annoyance.
When playing traffic noise and water, behavior types had no significant effect on OCV. OCV for
reading was lower than for other behaviors when playing air-conditioning noise, music, and speechwith statistical significance. This indicated that air-conditioning noise, music, and speech are more
likely to cause human discomfort and increase irritation when reading. Therefore, a more positive
acoustic environment should be provided in the office for people when reading. We can improve
human comfort while reading by playing water sounds and reducing air-conditioning noise andspeech, etc.

ACKNOWLEDGEMENTS

The authors would like to thank all the respondents for their time. Special thanks to the National
Natural Science Foundation of China (Project Number: 51878210, 51678180, 51308147) for theirsupport.

REFERENCES
1. Huang L, Yingxin Z, Ouyang Q, Bin C. A study on the effects of thermal, luminous, and acoustic
environments on indoor environmental comfort in offices. BUILD ENVIRON. 2012;49:304-9.
2. Yang W, Moon HJ. Cross-modal effects of noise and thermal conditions on indoor environmental
perception and speech recognition. APPL ACOUST. 2018;141:1-8.
3. Guan H, Hu S, Lu M, He M, Mao Z, Liu G. People's subjective and physiological responses to the
combined thermal-acoustic environments. BUILD ENVIRON. 2020;172:106709.
4. Yang W, Moon HJ. Combined effects of acoustic, thermal, and illumination conditions on the comfort
of discrete senses and overall indoor environment. BUILD ENVIRON. 2019;148:623-33.
5. Wu H, Sun X, Wu Y. Investigation of the relationships between thermal, acoustic, illuminous
environments and human perceptions. Journal of Building Engineering. 2020;32:101839.
6. Fanger PO, Breum NO, Jerking E. Can Colour and Noise Influence Man's Thermal Comfort?
ERGONOMICS. 1977 1977-01-01;20(1):11-8.
7. Tiller D, Wang LM, Musser A, Radik MJ, ^editors. Combined effects of noise and temperature on
human comfort and performance. Ashrae Conference; 2010. Pub Place; Year Published.
8. Nagano K, Horikoshi T. New index of combined effect of temperature and noise on human comfort:
summer experiments on hot ambient temperature and traffic noise. Archives of Complex Environmental
Studies. 2001(13):1-8.
9. Nagano K, Horikoshi T. New comfort index during combined conditions of moderate low ambient
temperature and traffic noise. Energy & Buildings. 2005;37(3):287-94.
10.Beers EA, Roemmich JN, Epstein LH, Horvath PJ. Increasing passive energy expenditure during
clerical work. EUR J APPL PHYSIOL. 2008;103(3):353-60.
11. Levine JA, Miller JM. The energy expenditure of using a \"walk-and-work\" desk for office workers
with obesity. BRIT J SPORT MED. 2007;41(9):558-61.
12.Cox RH, Guth J, Siekemeyer L, Kellems B, Brehm SB, Ohlinger CM. Metabolic cost and speech
quality while using an active workstation. J PHYS ACT HEALTH. 2011;8(3):332-9.
13.Reiff C, Marlatt K, Dengel DR. Difference in caloric expenditure in sitting versus standing desks. J
PHYS ACT HEALTH. 2012;9(7):1009.
14.Mansoubi M, Pearson N, Clemes SA, Biddle SJ, Bodicoat DH, Tolfrey K, et al. Energy expenditureduring common sitting and standing tasks: examining the 1.5 MET definition of sedentary behaviour.
BMC PUBLIC HEALTH. 2015;15(1).
15.ANSI/ASHRAE/IES Standard 55-2017. Thermal environmental conditions for human occupancy.
Atlanta, GA: American Society of Heating, Ventilation, Refrigerating and Air Conditioning Engineers.
2017.
16.Pronk NP, Katz AS, Lowry M, Payfer JR. Reducing Occupational Sitting Time and Improving Worker
Health: The Take-a-Stand Project, 2011. PREV CHRONIC DIS. 2012;9:110323.
17.Yang L, Gao S, Zhao S, Zhang H, Arens E, Zhai Y. Thermal comfort and physiological responses withstanding and treadmill workstations in summer. BUILD ENVIRON. 2020;185:107238.

18.Mcnall PE, Jaax R, Nevins RG. Thermal comfort conditions for three levels of activity. 1967.
19.Gonzalez RR. Exercise physiology and sensory responses. Studies in Environmental Science.
1981;10:123-44.
20.Gagge AP, Stolwijk JA, Saltin B. Comfort and thermal sensations and associated physiologicalresponses during exercise at various ambient temperatures. ENVIRON RES. 1969 1969-01-01;2(3):209.
21.Meng Q, An Y, Yang D. Effects of acoustic environment on design work performance based on
multitask visual cognitive performance in office space. BUILD ENVIRON. 2021;205:108296.
22.Kraus U, Breitner S, Hampel R, Wolf K, Cyrys J, Geruschkat U, et al. Individual daytime noise
exposure in different microenvironments. ENVIRON RES. 2015;140:479-87.
23.Soebarto V, Zhang H, Schiavon S. A thermal comfort environmental chamber study of older andyounger people. BUILD ENVIRON. 2019;155(MAY):1-14.
24.Hodder SG, Parsons K. The effects of solar radiation on thermal comfort. INT J BIOMETEOROL.
2007;51(3):233-50.
25.David, V., Budescu, Mark, I., Appelbaum. Variance Stabilizing Transformations and the Power of the F
Test. JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATS. 1981.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0050

Effects of Acoustic Environment on the Psychological State Based on Visual
Cognitive Work in Open-plan Office
Jiani JIANG1; Qi MENG2*
1,2

School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural
Human Settlement, China; Environment Science and Technology, Ministry of Industry and Information
Technology, China

ABSTRACT
Acoustic environment is one of the significant factors affecting work performance in open-plan office.
Previous studies focused mainly on the impact of sound environment on work efficiency, and lack ofmental activities. Therefore, this study aimed to investigate the effects of different sound source types,sound pressure levels (SPLs), and reverberation times (RTs) on work psychological states based on visual
cognitive work. Forty-three participants were recruited to complete the cognitive tasks and questionnaires
from the perspective of work emotion, motivation, and mental fatigue.The following results were found:first, sound source types had a significant impact on participants psychological state. Generally, apart frommusic sound, and natural sound, sound sources caused different degrees of annoyance, disturbance andfatigue to work performance, among which air-conditioning noise was the most disturbing one. Second,
work psychological states were significantly different under different SPLs of air-conditioning noise, with
the increase of SPL value, pleasure degree and work motivation decreased while the fatigue degreeincreased. Third, speech noise with different RTs had no significant effects on the psychological state. This
study shows that the acoustic design of sound sources and SPL are expected to adjust the employees
psychological state engaged in visual cognition work.
Keywords: open-plan office; sound environment; visual cognition; emotion; motivation; mental fatigue.

1. INTRODUCTION
Mental health problems are prevalent in all working populations around the world, a recent
review by the Organisation for Economic Cooperation and Development (OECD) is estimated that
20% of working populations in high income countries are affected by severe and moderate mental
health problems (1). Of those affected, it has been reported that common mental health problems,
including work-related stress, anxiety as well as depression, are estimated to account for 51% of
work-related ill-health, and 55% of work-related sickness absence (2). Besides, its annual incidence
and prevalence has further increased since the COVID19 pandemic (3). Physical environment is oneof the job stressors (4), empirical researches on the impact of environmental conditions on work
performance have been conducted since the early 20th century, a great amount literature reviews
highlight a clear effect of environmental conditions and design features on individual and businessperformance, as well as mental health problems (5). Among physical office design factors, acoustic
environment has been one of the considerable office design factors related to work performancesince the 1970s open-plan office layouts have effectively replaced private offices (6). Noise, as anenvironmental pollutant, is defined as “the unwanted, unpleasant or disagreeable sound that causes
discomfort to all living beings” (7). Statistical evidence from questionnaire surveys of employees
shows the significant correlation between dissatisfied acoustic conditions and sick building
syndrome symptoms (8) as well as discomfort (9, 10) and annoyance (11). Most studies about noise
has been shown to produce many emotional reactions (e.g., annoyance, fear, agitation, helplessness,anxiety, stress, anger), besides, higher noise level is significantly associated with mental disorders
1
2

21b334003@stu.hit.edu.cn

mengq@hit.edu.cn (corresponding author)such as fatigue, depression and nervousness (12).There will be some acute effects of office noise(e.g., irritation, problems to concentrate) showing up directly whereas chronic or more indirect
effects (e.g., fatigue, stress, burnout symptoms) may show up in the long run (13). It can be seen thatmental health is a significant field in office environment, however, researches about acoustic
environment in open-plan offices mostly focus on the effects of sound on work performance as well
as overall evaluation of indoor environment, lack of discussions about specific psychological statesof employees.
Studies about office acoustic environment usually focus on three aspects: sound source type,sound pressure level (SPL), and reverberation time (RT). In terms of sound source type, the acoustic
environment of office can be composed of external and internal sound sources (14), of which
external sound sources mainly include traffic noise, construction sound and natural sound (15).
Internal sound sources mainly include noise generated from air-conditioning equipment, irrelevant
speech nearby and some task-specific non-speech sounds (e.g., mouse clicks, keyboard operation,
printer related noise) (16), among which, intelligible conversations were the source of noise that wasconsidered the most annoying. In terms of SPL, field measurements of multiple open-plan offices
indicate that the average SPL of the work space is 53.6 dBA (16), and work performance will
decrease with the increase of SPL (17). In terms of RT, the effects of RT on work performance arestill uncertain, some studies indicate that the memory accuracy decreases with the increase of RT(18), while some studies find that the increase in RT of speech will shorten the reaction time andincrease the memory accuracy (19). However, the effect of acoustic properties on psychologicalstates has been seldom studied.
A qualitative research found that motivation, attitudes, and mental fatigue were correlated withwork performance by content analysis (20). Among which, motivation is often defined as an internal
force that directs, energizes, and sustains behavior over time and across changing circumstances (21).
The importance of work motivation on cognitive task behaviors in terms of generative-defensiveorientation, effort, and persistence, has already been emphasized in prominent theories, such as goal
setting theory, control theory and expectancy theory (22). Mental fatigue refers to the feeling thatpeople experience after or during prolonged period of cognitive activity, and there is a substantial
increase in fatigue related complaints since the extent of work has changed from predominantly
physical in nature to being more mentally oriented (23). The cognitive effect shows that increased
distraction, reduced discrimination and limited attention caused by mental fatigue usually leads to adecrease in work efficiency (24). Attitude is a statement or evaluative consideration regarding
objects, people, or events, which associated to organizational values, and can be described as apositive or negative psychological state (20). Besides, emotions are, in effect, organizedcognitive-motivational- relational configurations whose status changes with changes in the
person-environment relationship as this is perceived and evaluated (25). Researches argue that
individual’s goal-directed behaviors can be energized by emotion, positive emotions showing greateralignment with approach motivation and negative emotions show greater alignment with avoidance
motivation (16). Since work attitude is more closely related to the organization culture, emotion isbetter to describe the psychological activities of employees from the individual level than attitude.
Cumulative risks from stressor exposures in the workplace have been taken seriously (26), however,results of a mental health quality assessment in workplace indicate that low scores are often due to alack of focus on prevention rather the treatment (1). Therefore, it’s necessary to detect the mental
activities of employees during work under different acoustic environment rather than measuring themental health of employees directly.
It can be seen that mental health of employees is a social problem widely concerned andacoustic environment as one of the physical environmental factors also plays a significant role on it.
However, related studies mainly focus on the influence of indoor acoustic environment on work
performance and on the overall indoor assessments about satisfaction or comfort, research at thelevel of psychological states is lacking. Therefore, this paper aims at exploring the influence ofdifferent sound source types, SPLs, and RTs of indoor acoustic environment on employees’
psychological states from three dimensions, work emotion, motivation, and mental fatigue.

2. METHODOLOGY
2.1 Case Site
Since this study involved the setting of RTs, which was not conductive for accommodation inexperiments. Considering the experimental convenience, this study selected a ordinary room
(5.6m*3.3m*3.0m) for laboratory experiment and the spatial and acoustic characteristics were
adjusted by changing acoustic parameters of sound materials. Headphones were used to provide thesound environment of open-plan offices during the experiments.
2.2 Acoustic Environment Setting
In terms of sound source type, with comprehensive consideration of the actual situation as well as
the restorative effect of acoustic environment, five representative sound sources (speech,
air-conditioning noise, traffic noise, natural sound, and music) were selected as sound samples inthis paper. Among which, ‘speech’ was a Chinese-language recording introducing architecturaldesign, which was carried out in the anechoic chamber, in female voice, at a constant speed (4 words
per second). ‘Air conditioning noise’ refers to the low frequency noise generated by the air
conditioner recorded in the anechoic chamber. ‘Traffic noise’ referred to the noise from main street(with vehicles with an average speed of 50 km/h). ‘Natural sound’ was a recording of a flowing
stream. ‘Music’ was Souvenirs d’enfance, calm music without lyrics and obvious emotional value(27), downloaded from music material library (28). The HEAD multichannel acoustic system wasused to collect binaural recordings, the signal recording equipment was placed 1.5 m above the floor
and 1 m away from wall. A typical 5 min audio clip from each recordings was selected as the soundmaterial.
In terms of SPLs, the ambient environment is around 40 dBA under quiet condition (29), thehuman auditory feedback levels are 5 dBA (30) and the acceptable SPL range of noise in open-plan
offices is below 70 dBA (29). Besides, a previous study reported that the emotions of the respondents
would be awakened and they might even begin to feel annoyed when the SPL exceeds 50 dBA (31),and low frequency noise is the main source of office noise (32). Therefore, 50 dBA was set forinvestigation of effects of sound source type and RTs, air-conditioning noise from 45 to 65 dBA witha 5 dBA step size were selected for investigation of the effects of SPLs. Adobe Audition software
was used to adjust the SPLs of the five sound samples to the corresponding SPLs with sensitivity of
0.5 dBA.
In terms of RTs, the RT in the office space ranges from 0.3s to 1.5s in general (19), and the speechtransmission index (STI) of irrelevant speech is an effective predictor of work performance (33).
Therefore, in this study, the RTs of speech from 0.3 to 1.5s with 0.3s as the step length were set forcomparison. Adobe Audition software was used to adjust the RTs of the two aforementioned soundsamples to ensure that the RT was the only variable in the experiment.
2.3 Questionnaire Survey
A total of 43 graduate students majoring in architecture were recruited to complete the cognitive
tasks and questionnaires. The questionnaire consisted of two parts, as shown in Table 1. The firstpart was about basic information, including name, gender and age. The second part was to fill in theevaluations of psychological states in terms of work emotion, motivation, and mental fatigue after
each task under different sound conditions, on a 9 - point scale.
Table 1 – The structure and descriptions of questionnaire
Structure
Basic
information

Questions

Description

Name, gender, Age
1 - very annoyed; 5 - Neither annoyed nor

Work emotion

How do you feel during the task?

Work

How did the sound environment

1 - very disturbing; 5 - Neither disturbing nor

motivationinfluence on your motivation?

stimulative; 9 - very stimulative

What’s the degree of your mental

1 - very tired; 5 - Neither tired nor relaxed; 9 -Mental fatigue

fatigue now?

pleasant; 9 - very pleasantvery relaxed2.4 Cognitive task
Visual cognitive task covers visual search tasks, graphical memory tasks, and location memorytasks, which requires visual perception as well as the ability of recognition (19). Since visual
cognitive task reflects part of the design work content, therefore it was selected as the cognitive
work in this study. The task was a multi task combined with search task and location memory task aswell as the Landolt ring task, and the Landolt ring was the target and the enclosed ring was theinterference term. The task content involved three parts: judging the presence of the target,
remembering the quadrant of the target, pointing the opening direction of the target, as shown in
Figure 1.

Figure 1 – The content of task
2.5 Experimental Design
The experiment was divided into three sections based on the sound source types, SPLs, and RTs.
Section1, sound source types, consisted of five sound samples (SPL=50 dBA, RT=0.3s) : speech (S),
air-conditioning noise (A), traffic noise (T), natural sound (N), and music (M). Section2 included
five levels of SPLs: 45 dBA, 50 dBA, 55 dBA, 60 dBA, 65 dBA. Section3 included five levels of
RTs: 0.3s, 0.6s, 0.9s, 1.2s. 1.5s. Before the experiment, visual-audio tests were taken to ensure allparticipants with normal visual and hearing abilities, and it took 2-3 min for participants to getfamiliar to the requests of the visual cognitive tests. After the experimental preparation stage,
participants were asked to fill in their basic information in 1-2 min. Then, visual cognitive task
started, it took 5 min to complete each trail under specific sound environment. Finally, participants
rated for the evaluations of psychological states in terms of work emotion, motivation and mental
fatigue after each trail. There was a 5-minute interval between each trail.
2.6 Data Analysis
The professional statistics commercial software SPSS 23.0 (Statistical Product and Service
Solutions, IBM, America) was used to conduct statistical analysis based on the data obtained fromthe experiment. Firstly, the Kolmogorov-Smirnov test (K-S test) was used to analyse the normalityof the experimental data. The analysis results showed that the data didn’t satisfy the normal
distribution. Then the nonparametric Kruskal-Wallis test (K-W test) was used to analyse thedifferences between groups of experimental data under different sound environments. If p < 0.05,
Nemenyi’s method would be employed to conduct multiple comparison analyses on specificdifferences between sound sources.

3. Results
3.1 Effects of sound source type on psychological states
The relationship between sound source types and the mean values of psychological states
evaluation in terms of work emotion, motivation and fatigue is shown in Figure 2. Results of theK-W test showed a significant effect of sound source type on the evaluation of psychological statesof all dimensions (p < 0.05). Of the five sound source types, only natural sound and music naturalsound showed positive effects on psychological states. Among which, natural sound may help
regulating emotion (5.49) and fatigue (5.86), and music effectively improved emotion (6.26),

motivation (5.26) as well as fatigue (6.35). It was noticeable that the psychological states ofparticipants were worst under air-conditioning noise, the mean values of emotion (3.49), motivation(3.42) and fatigue (3.98) were lowest compared to speech and traffic noise.

Figure 2 – Mean values of psychological states under different sound source types
3.2 Effects of SPLs on psychological states
The relationship between SPLs from 45 dBA to 65 dBA and the mean values of psychologicalstates evaluation in terms of work emotion, motivation and fatigue is shown in Figure 3. A
continuous decrease can be seen of the mean values of emotion and motivation evaluation, with therise of SPL, it dropped from 4.14 to 2.09, and from 4.37 to 2.70 respectively. When SPL reached 50
dBA the average degrees of motivation decreased from 4.30 to 3.42, and the mean value basicallyremain unchanged from 50 dBA to 60 dBA, until a great drop from 3.28 to 2.40 when SPL reached
65 dBA. Results of the K-W test showed a significant effect of SPLs on the evaluation of
psychological states of all dimensions (p < 0.05). When SPL rose to 50 dBA, the motivation of thesubjects began to deteriorate, while the differences of the evaluation of emotion and fatigue were notthat significant. When SPL reached 55 dBA, evaluations of emotion, motivation and fatigue all got
worse compared to 45 dBA significantly. And there was a significant difference of psychologicalstates between SPL of 55 dBA and 65 dBA. It can be estimated that considering employees’
psychological states, the acceptable SPL of open-plan offices should be below 55 dBA, preferably nomore than 50 dBA.

Figure 3 – Mean values of psychological states under different SPLs

3.3 Effects of RTs on psychological states
The relationship between RTs from 0.3s to 1.5s and the mean values of psychological states
evaluation in terms of work emotion, motivation and fatigue is shown in Figure 4. Results of theK-W test showed that there was no significant effect of RTs on the evaluation of psychological statesof all dimensions (p > 0.05). Under the sound of speech (50 dBA) of RTs from 0.3s to 1.5s, all of themean values of psychological states evaluation were around 4, with a slightly negative effect.

Figure 4 – Mean values of psychological states under different RTs

4. CONCLUSIONSWith the collection of the typical sound sources in the open-plan office and a subjective
questionnaire survey of psychological states based on a visual cognitive task, this study examined
the influence of the acoustic environment on employees’ work emotion, motivation and fatigue.
Based on the results, several conclusions can be drawn. First, sound source type was significantly
associated with psychological states, among the five experimental sound environment, music seemed
to be the most appropriate sound source to improve employees’ emotion, motivation and to relieve
fatigue. Whereas, low frequency noise, such as air-conditioning noise, brought the worst effects on
psychological states. Second, work psychological states were significantly different under different
SPLs of air-conditioning noise, with the increase of SPL value, mean values of emotion andmotivation decreased while the fatigue degree increased. It can be estimated that the acceptable SPLof open-plan offices should be below 55 dBA, preferably no more than 50 dBA. Third, speech noisewith different RTs had no significant effects on the psychological state. The present study can help
improve the psychological states of the employees who engaged in the visual cognitive related workin terms of sound source type and SPLs.

ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China (Project Number:
51878210, 51678180, 51308147).

REFERENCES
1. Memish K, Martin A, Bartlett L, Dawkins S, Sanderson K. Workplace mental health: An international
review of guidelines. Prev. Med., 2017(101):213-222.
2. HSE. Health and Safety Statistics 2020 [Internet]. Health and Safety Executive, 2020.
www.hse.gov.uk/statistics (20 January 2021, date last accessed).
3. Palka G, Sen D. Occupational health management of work-related stress: guidelines versus practice.
Occup. Med., 2021;71(3):154-160.
4. Long CS, Jusoh A, Ajagbe MA, Ghee LC. A Review on Job Stressor in the Perspective of Health Care

Industry. Research Journal of Recent Sciences, 2013; 2(3):81-86.
5. Oseland N, Burton A. Quantifying the impact of environmental conditions on worker performance for
inputting to a business case to justify enhanced workplace design features. J. Build. Surv.
2012(15):151-165.
6. Vassie K, Richardson M. Effect of self-adjustable masking noise on open-plan office worker's
concentration, task performance and attitudes. Appl. Acoust. 2017(119):119-127.
7. Golbidi S, Li H, Laher I. Oxidative stress: a unifying mechanism for cell damage induced by noise,(water-pipe) smoking and emotional stress – Therapeutic strategies targeting redox imbalance. Antioxid.
Redox Signal., 2017; 28(9).
8. Azuma K, Ikeda K, Kagi N, Yanag U, Osawa H. Prevalence and risk factors associated with nonspecificbuilding‐related symptoms in office employees in japan: relationships between work environment,indoor air quality, and occupational stress. Indoor Air, 2015; 25(5):499-511.
9. Attia S, Garat S, Cool M. Development and validation of a survey for well-being and interaction
assessment by occupants in office buildings with adaptive facades. Build. Environ., 2019(157):268-276.
10. Choi J, Lee K. Investigation of the feasibility of POE methodology for a modern commercial officebuilding. Build. Environ., 2018; 143(OCT.):591-604.
11. Chevret P. Effect of noise on comfort in open-plan offices: application of an assessment questionnaire.
Ergonomics, 2017; 60(1):1-31.
12. Stansfeld S, Clark C. Mental Health Effects of Noise. Encyclopedia of Environmental Health, 2011.
p.683-689.
13. Jahncke H. Cognitive Performance and Restoration in Open-Plan Office Noise. Tillmpad Psykologi,
2012.
14. Zhang M, Kang J, Jiao F. A social survey on the noise impact in open-plan working environments in
China. Sci. Total Environ., 2012(438):517-526.
15. Jo HI, Kim HW, Jeon JY. Investigation of work performance in open-plan office with soundscapevariation using Virtual reality. INTER-NOISE and NOISE-CON Congress and Conference Proceedings.
2019.
16. Yadav M, Cabrera D, Kim J, Fels J, Dear R. Sound in occupied open-plan offices: Objective metrics
with a review of historical perspectives. Appl. Acoust., 2021(177):107943.
17.Nasiri P, Monazzam M, Mohammadian F, Zakerian S., Azam K. Assessment of the effects of different
noise levels on mental performance of individual doing mathematical calculation. International
Research Journal of Applied and Basic Sciences, 2014; 8(7):933-939.
18.Ljung R, Kjellberg A. Recall of spoken words presented with a prolonged reverberation time. 9thInternational Congress on Noise as a Public Health Problem (ICBEN), 2008.
19.Meng Q, An Y, Yang D. Effects of acoustic environment on design work performance based on
multitask visual cognitive performance in office space. Build. Environ., 2021, 205(6):108296.
20.Subandowo M. Multicultural Education Facets Of Motivation, Attitude, And Non-Physical Fatigue With
Work Productivity In Colleges. Journal for Multicultural Education, 2021; 7(7):452-461.
21.Diefendorff JM, Seaton GA. Work Motivation. International Encyclopedia of the Social & Behavioral
Sciences (Second Edition), 2015(25):680-686.
22.Seo MG , Bartunek JM , Barrett LF. The role of affective experience in work motivation: Test of aconceptual model. J. Organiz. Behav., 2010, 31(7):951-968.
23. Boksem MAS , Tops M. Mental fatigue: Costs and benefits. Brain Res. Rev., 2008; 59(1):125-139.
24. Lock AM, Bonetti DL, Campbell A . The psychological and physiological health effects of fatigue.
Occup. Med., 2018(8):502-511.
25. Rolls ET. The Orbitofrontal Cortex and Emotion in Health and Disease, Including Depression.
Neuropsychologia, 2019(128):14-43.
26. Fox MA, Niemeier RT, Hudson N, Siegel MR, Dotson GS. Cumulative Risks from Stressor Exposuresand Personal Risk Factors in the Workplace: Examples from a Scoping Review. Int. J. Environ. Res.
Public Health 2021(18):5850.
27.Meng Q, Jiang J, Liu F, Xu X, Effects of the musical sound environment on communicating emotion,
Int. J. Environ. Res. Publ. Health, 2020; 17 (2499).
28.Shu S, Ma H. Restorative Effects of Classroom Soundscapes on Children’s Cognitive Performance. Int.
J. Environ. Res. Public Health, 2019, 16, 293.
29.Liu H, Qin J, He H. Explore which industries are suitable for open office: Through an experiment on theimpact of noise on individual job performance. Noise Control Eng. J., 2019; 67(6):422-437.
30.Bottalico P, Cutiva L, Hunter EJ. Vocal effort and fatigue in virtual room acoustics. The Journal of theAcoustical Society of America, 2016; 140(4):3126-3126.
31.Virjonen P, Keränen J, Hongisto V. Determination of Acoustical Conditions in Open-Plan Offices:
Proposal for New Measurement Method and Target Values. Acta Acust. United Ac., 2009;
95(2):279-290(12).
32.Abbasi AM, Motamedzade M, Aliabadi M, Golmohammadi R, Tapak L. Study of the physiological andmental health effects caused by exposure to low-frequency noise in a simulated control room. Build.
Acoust., 2018; 25(2):1351010X1877951.
33.Varjo J, Hongisto V, Haapakangas A, Maula H, Koskela H, Hyona J. Simultaneous effects of irrelevant
speech, temperature and ventilation rate on performance and satisfaction in open-plan offices. J.
Environ. Psychol., 2015; 44(1):16-33.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0070

Effects of spatial-temporal variation of vegetation structure on
sound perception in urban parksYi XIANG1,3,4; Qi MENG2,3
12

School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural

Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology,
China
3

Anhui jianzhu university; Key Laboratory of Architectural Acoustic Environment of Anhui Higher Education
Institutes, China
4

College of Landscape Architecture and Art, Northwest A&F University, China

ABSTRACT
Spatial and temporal variation of vegetation structure is an important factor affecting tourists’ visiting
experience. In this study, 10 urban parks in Xi’an were investigated by field survey. According to the location,function and vegetation configuration, the park spaces were divided into different types and the influence of
spatiotemporal variation of vegetation structure on tourists’ soundscape perception was explored. The results
showed that: the perception of human sounds first increased and then decreased with canopy coverage witha turning point of 22%; the perception of natural sounds showed an increase and then a decrease in a parabolic
form with canopy coverage of tree/shrubs, in which the axis of symmetry is coverage = 40%; the perceptionof traffic noise linearly decreased with the increasing canopy coverage of tree/shrubs. The canopy coverageof tree/shrubs in different spaces had different influences on natural sounds, human sounds and traffic noise.
The perceived extent of sounds in each period showed human sounds > traffic noise > natural sounds. In
different canopy coverage of tree/shrubs, traffic noise showed a trend of changing with time. The conclusion
provides a theoretical reference for improving the sound perception of urban parks from the perspective of
vegetation structure.
Keywords: Soundscape, Vegetation structure, Spatial-temporal variation

1. INTRODUCTION
Urban parks have been regarded as an important element of sustainable and healthy cities, which
can effectively promote healthy and cultural urban life and public well -being (1,2), and increasingly
become essential infrastructure for residents' leisure and entertainment life (3,4). Previous studies
often considered the visual elements of the park landscape, but ignored the park soundscapecharacteristics (5-7). International standard ISO 12913-1 (2014) defines soundscapes as “acoustic
environment as perceived or experienced and/or understood by a person or people, in context” (8).
Studies have shown that soundscape plays a crucial role in urban public park sightseeing experience(9,10). Moreover, soundscape is closely related to landscape environment. The environmental
characteristics of urban park landscape would affect the soundscape experience (11,12).
As an important feature of landscape, the influence of canopy coverage of tree/shrubs on urban
park soundscape experience remains to be explored. Chitra et al. found that dense vegetation had agreater regulating effect on environmental soundscape than shrubs and ground cover (7). Other studies
also found that there was a significant relationship between visual masking of vegetation and noise
annoyance (13, 14). These studies indicated that the coverage of trees and shrubs, as a feature ofvegetation structure, can significantly influence the soundscape (15). Therefore, the influence of
canopy coverage of tree/shrubs on the soundscape in urban parks needs to be further explored.
1
2

21B934006@stu.hit.edu.cn
mengq@hit.edu.cn

Whether there are differences in soundscape experience in different spaces in urban parks and theinfluence of canopy coverage of tree/shrubs in different spaces on soundscape has also not been fully
explored. Existing studies often select a variety of representative sample points in urban parks to
conduct soundscape research, so as to represent the overall soundscape of urban parks (16, 17).
However, spaces with different characteristics may lead to differences in tourist activities, resulting
in differences in soundscape (18). For example, tourists choose different activities in the park's
waterfront space and non-waterfront space, staying space and traveling space, open space and closed
space, which may lead to different soundscape. Understanding the soundscape of different spaces inurban parks is beneficial to the design and transformation of park interior space. Vegetation structure
is an important factor to distinguish different urban park spaces, and its influence on soundscape is
not clear.
In addition, for the urban park space with different vegetation structure, the change of people's
perception of sound in a day is not clear. Studies have shown that soundscape changes during a d ay
are influenced by the surrounding environment and people's behavioral activities (19, 20). Liu et al.
also discussed the temporal and spatial changes of soundscape on a large scale and found that thetemporal changes of soundscape were mainly driven by urban activities, among which humanactivities were the main factor (21). However, for the urban park space, which has an important
relationship with the daily recreation activities of human beings, the changes of its soundscape during
the day have not been understood in detail.
Therefore, the purpose of this study is to first understand the impact of vegetation structure on
sound perception in urban parks, then to understand the difference of sound perception in different
types of space and the impact of vegetation structure on this difference, and finally to explore theinfluence of vegetation structure on change of sound perception in urban park spaces in a day.

2. Method
2.1 Study area
This study was carried out in Xi’an (33◦42′~ 34◦45′N, 107◦40′~109◦49′E), a city with a rich history
and many urban parks in China. Ten different typical urban park spaces were selected in this study,
including waterfront (W) and non-waterfront (NW) space, staying (S) and traveling (T) space, urban
high-density (H) and low-density (L) space, enclosed space (E), semi-enclosed (SE) space, scatteredspace (SC) and clustered space (C) (Fig. 1). These spaces contain a wealth of sound sources, including
natural sounds (wind, birds, water, etc.), human sounds (talking, singing, music), traffic noises(honking, engine, brake).

Figure 1 – Locations and sample photos of study area

2.2 Data collection
The field survey was conducted on sunny days from September to October in 2019. The daily
questionnaire was distributed successively from 10:00 a.m. to 6:00 p.m. (morning: 10:00-12:00, noon:
12:00-14:00, afternoon: 14:00-16:00, evening: 16:00-18:00). Respondents were randomly selectedfrom users in each sampling site of the selected places. They were distributed the electronicquestionnaires which could be completed via android pads. All respondents were instructed tocomplete the questionnaire independently. After completing the questionnaire, each respondent would
be given a small gift such as a small electric fan or stationery product as a reward. The questionnaire
included respondents' perception of natural sounds (NS), human sounds (HS) and traffic noise (TS)sources. Five-level Likert scale was used for evaluation.
2.3 Data analysis
A total of 2034 valid questionnaires were collected, with an effective rate of 95%. Firstly, a linear
model was used to fit the relationship between canopy coverage and sound perception. Then, a generallinear model was used to explore the difference of sound perception in different types of space, andcorrelation analysis was used to explore the impact of vegetation structure on sound perception indifferent types of space. When there was a significant correlation between vegetation structure andsound perception, the relationship between vegetation structure and sound perception was linearly
fitted, and the fitting model with the highest R 2 was selected. Finally, the relationship between canopy
coverage and sound perception during a day was investigated by three-dimensional surface fitting.

3. Results
3.1 Relationship between vegetation structure and sound perception
The results of linear fitting showed that overall canopy coverage of tree/shrubs was negatively
correlated with traffic noise (R 2 =0.27), and had a parabolic relationship with natural sound perception(R2 =0.26). When canopy coverage was lower than 22%, there was a linear positive correlation
between canopy coverage and human sound perception (R 2 =0.83); when canopy coverage was higher
than 22%, there was a quadratic relationship between canopy coverage and human sound perception(R2 =0.54, Fig. 2).
3.4

3.2

3.2
3.0

3.0

y=0.71x+2.83
r2=0.83

3.0

2.8

2.6
2.4

y = 0.52x2 - 0.98x + 3.11
r²= 0.54

2.8

Score

y=-0.94x+2.99
r2=0.27

Score

Score

2.8

y=-1.58x2+1.36x+2.25
r2=0.26

2.6

2.4

2.2
2.6

2.0

2.2

1.8
0.0

0.2

0.4
0.6
CC(TS)

0.8

1.0

0.0

0.2

0.4
CC(HS)

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

CC(NS)

Figure 2 – Relationship between canopy coverage of tree/shrubs and sound perception
3.2 Relationship between vegetation structure and sound perception in different spaces
The results of the general linear model showed that there were significant differences between
natural sounds and human sounds perception for waterfront space and non-waterfront space, highdensity space and low-density space (p < 0.05), there was no significant difference in traffic noise.
And there were significant differences between natural sounds and traffic noise for staying space andtraveling space (p < 0.05), there was no significant difference in human sounds perception. For the
space enclosed by different vegetation forms, there were significant differences in human sounds andtraffic noise perception (p < 0.05), and there was no significant difference in natural so unds (Table 1,
Fig. 3).
The effects of vegetation structure on sound perception in different spaces showed that canopy
coverage was significantly correlated with natural sounds perception in staying space, traveling spaceand semi-enclosed space (p < 0.01); In waterfront space, low-density space, enclosed space andclustered space, canopy coverage was significantly correlated with human sounds perception (p <0.01); In addition to semi-enclosed space and clustered space, canopy coverage was significantlycorrelated with traffic noise perception (p < 0.01, Table 2). In addition, the perception of humansounds and traffic noise in waterfront space, traffic noise in non-waterfront space, human sounds in
enclosed space, natural sounds in semi-enclosed space and canopy coverage showed a quadratic
function relationship, and the perception of sounds and canopy coverage in other spaces showed a
simple linear relationship. And only in the clustered space, human sounds perception and canopy
coverage showed a positive linear correlation (Fig. 4).
Table 1 – Perception differences of sound sources in different spaces
Type of space

Sound source

Ⅲ-SS

DF

NS

2.25

1

HS

3.57

TS

W-NW

S-T

H-L

E-SE-SC-C

Mean

F

Sig.

2.25

4.43

0.03

1

3.57

8.03

<0.01

0.82

1

0.82

0.98

0.32

NS

3.01

1

3.01

6.24

0.01

HS

0.31

1

0.31

0.68

0.41

TS

24.11

1

24.11

30.98

<0.01

NS

4.38

1

4.38

8.79

<0.01

HS

2.47

1

2.47

5.52

0.02

TS

0.35

1

0.35

0.43

0.51

NS

2.90

3

0.97

1.96

0.11

HS

3.05

3

3.05

4.33

0.04

TS

12.45

3

4.15

5.12

<0.01

6

Square6high density
low density

waterfront
non-waterfront

4

Score

Score

4

2

2

0

0
HS

NS

6

6

staying
traveling

TS

NS

HS

TS

enclosedsemi-enclosed
scatteredclustered

4

Score

Score

4

2

2

0

0
HS

NS

TS

HS

TS

NS

Figure 3 – Sound perception in different spaces
Table 2 – Relationship between sound source and canopy coverage in different space
Sound
W
NW
S
T
H
L
E
SE
SC
C
source
NS

-

-

-0.11

-0.08

-

-

-

-0.24

-

-

-0.19

HS

**(e)

-

-

-

-0.16
**(f)

-

**(a)

TS

**(d)

-0.33

-0.20

**(b)

**(c)

-

**(l)
-0.16

-0.10

**(h)

**(j)

-0.31

-0.22

-0.29

**(g)

**(i)

**(k)

-

0.14

-

**(n)

-0.27

-

-

**(m)

**p < 0.01. -No significant correlation. The bracket numbers correspond to Figure 4.
方程
绘图
权重
截距
B1
B2
残差平方和
R平方(COD)
调整后R平方

3.5

3.4
3.3

y=1.38x -1.99x+3.51
r2=0.373.0

y=-3.38x2+1.85x+2.81
r2=0.74

3.2
3.0

Score

2

Score

3.1

BY
Sheet1列BY的多项式拟合

3.4

3.0

3.2

Score

y = Intercept + B1*x^1 + B2*x^2BX不加权
3.506 ±0.40905-1.9941 ±1.8131
1.38691 ±1.70214
0.21697
0.368880.11643y=5.0x2-6.97x+4.48
r2=0.67

2.5

2.9

2.8
2.6
2.4

2.8

2.0

2.2

2.7
0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.2

0.9

0.4

0.6

0.8

2.0

1.0

0.0

0.1 0.2

0.3

CC(b)

CC(a)
方程

y = a + b*x

绘图

CC

2.55

0.4 0.5

0.6 0.7

0.8

CC(c)

3.5

不加权
CC
3.53401 ±0.24965-1.79365 ±0.45156
Sheet1列CC的线性拟合
0.00514
2.50Pearson's r Sheet1列CC的线性拟合-0.94207
权重

截距

斜率

残差平方和

R平方(COD)

0.8875

调整后R平方0.83125

2.45

2.55

2.40

2.50
y=-1.79x+3.53
r2=0.882.45

3.0

y=-0.25x+2.48
r2=0.19

2.35

2.5

2.30

y=1.6x+1.41
r2=0.66y=-1.14x+3.32
r2=0.50

Score

2.60

Score

Score

2.65

2.25

2.40

2.20
0.50

0.55

0.60

0.65

0.70

0.75

0.1

0.80

0.2 0.3

0.4

0.7

2.0
0.0

0.8 0.9

0.2

0.4

3.4

0.8

1.0

3.5

3.2

3.2

0.6
CC(f)

CC(e)

CC(d)

3.1

3.0

y=-1.35x+3.36
r2=0.43

2.6

2.9

2.7

2.2

2.6

2.0

2.5

1.8

2.4
0.3

0.4

0.5

0.6

0.7

0.8

y=-0.70x+3.06
r2=0.54

2.8

2.4

0.2

3.0

3.0

Score

2.8

Score

0.5 0.6

0.9

Score

2.35
0.45

y=-1.30x+3.10
r2=0.43

2.5

2.0

0.0

0.1 0.2

0.3

CC(g)

0.4 0.5

0.6 0.7

0.0

0.8

0.2

0.4

0.6

0.8

CC(i)

CC(h)

3.5
3.4

3.0
y=2.15x2-2.92x+3.78
r2=0.47

Score

2.8

y=-1.79x+3.71
r2=0.47
2.5

2.6
2.4
2.2

2.6

2.0

2.0

2.4
0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.2

0.9

0.4

0.6

0.8

1.0

0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65

CC(k)

CC(j)

3.4

CC(l)

3.25
3.20

3.2

方程

3.15

y=-0.75x+2.89
r2=0.32

3.0

绘图

3.10

Score

2.8

Score

Score

3.0

y=-13.45x2+12.12x+0.05
r2=0.74

2.8

3.0

Score

3.2

2.6
2.4

3.05

CT
不加权

截距

2.82174 ±0.02993

斜率0.80249 ±0.10726

残差平方和

0.00603

Pearson's r

0.96608

R平方(COD)

0.93331

调整后R平方0.91663

Y=0.80X+2.82
R2=0.93

3.00
2.95

2.2

y = a + b*x

权重

2.90

2.0

2.85

1.8

2.80

0.0

0.1 0.2

0.3

0.4 0.5CC(m)0.6 0.7

0.8

0.0

0.1

0.2

0.3

0.4

0.5

CC(n)

Figure 4 – The specific relationship between sound source and canopy coverage3.3 Relationship between vegetation structure and sound perception at different periods
On the whole, human sounds perception in urban parks was always the highest in a day, while
natural sounds perception was always the lowest in a day. Human sounds perception was the highest
in morning and evening. Traffic noise perception was the highest at noon and evening. The highest
level of natural sounds perception occurred in the morning (Fig. 5).
Three-dimensional surface figure showed that, the canopy coverage is higher, the lower the overall
traffic noise perception. Under different canopy coverage, the traffic noise showed that the trend of
higher perceived in the afternoon and evening (Fig. 6). Human sounds and natural sounds showed nosignificant change trend with time, but human sounds perception decreased with canopy coverageincreasing at all periods of the day.
NS
HS
TS

3.0

Score

2.9
2.8
2.7
2.6
2.5
2.4
10:00-12:00 12:00-14:00 14:00-16:00 16:00-18:00
time

Figure 5 – Temporal variation of sound source perception

Figure 6 – Relationship between vegetation structure and sound perception at different periods

4. CONCLUSIONSIn this study, we investigated the influence of vegetation structure on the sound perception of urban
parks by questionnaire survey of different urban park spaces. The results showed that: in terms ofspace, as a whole, the perception of human sounds increased with canopy coverage when the canopy
coverage of tree/shrubs is less than 22% and decreased with canopy coverage when the canopy
coverage of tree/shrubs is greater than 22%; the perception of natural sounds showed an increase andthen a decrease in a parabolic form with canopy coverage of tree/shrubs, in which the axis of symmetry
is coverage = 40%; the perception of traffic noise linearly decreased with the increasing canopy
coverage of tree/shrubs. The canopy coverage of tree/shrubs in different spaces had different
influences on natural sounds, human sounds and traffic noise. The perception of sounds showed adecreasing trend with increasing canopy coverage of tree/shrubs, while the human sounds in clusteredspace linearly increased with increasing canopy coverage of tree/shrubs. In terms of time, as a whole,
the perceived extent of sounds in each period showed human sounds > traffic noise > natural sounds.
In different canopy coverage of tree/shrubs, traffic noise showed a trend of changing with time, while
human sounds and natural sounds showed no significant change with time. The conclusion provides atheoretical reference for improving the sound perception of urban parks from the perspective of
vegetation structure.
Future studies can further investigate the relationship between vegetation structure and soundperception in other urban parks, as well as the relationship between vegetation structure and othersoundscape indicators. In addition, future studies can further distinguish the perception differences ofpeople with demographic backgrounds in different spaces. What’s more, future studies can also
investigate the differences in the perception of soundscape among people with different activities indifferent spaces.

ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China [grant number
31971720, 31971722, 5217080336], this work was supported by the Open Fund Project of Anhui
Provincial Key Laboratory of Building Acoustic Environment [grant number AAE2021ZD03].

REFERENCES
1. Brown G, Schebella MF, Weber D. Using participatory GIS to measure physical activity and urban park
benefits. Landscape and Urban Planning. 2014;121:34-44.
2. Navarrete-Hernandez P, Laffan K. A greener urban environment: Designing green infrastructureinterventions to promote citizens’ subjective wellbeing. Landscape and Urban Planning.
2019;191:103618.
3. Doll C, Polyakov M, Pannell DJ, Burton MP. Rethinking urban park irrigation under climate change.
Journal of Environmental Management. 2022;314:115012.
4. Chiesura A. The role of urban parks for the sustainable city. Landscape and Urban Planning.
2004;68(1):129-38.
5. Pheasant R, Horoshenkov K, Watts G, Barrett B. The acoustic and visual factors influencing theconstruction of tranquil space in urban and rural environments tranquil spaces-quiet places? The Journalof the Acoustical Society of America. 2008;123(3):1446-57.
6. Kang J. From understanding to designing soundscapes. Front Archit Civil Eng Chin. 2010;4(4):15.
7. Chitra B, Jain M, Chundelli FA. Understanding the soundscape environment of an urban park throughlandscape elements. Environmental Technology & Innovation. 2020;19:100998.
8. International Organization for Standardization (ISO). ISO 12913–1: 2014 Acoustics—Soundscape—Part
1: Definition and Conceptual Framework. ISO, Geneva, Switzerland. 2014.
9. Krause B, Gage SH, Joo WJLE. Measuring and interpreting the temporal variability in the soundscape atfour places in Sequoia National Park. Landscape Ecology. 2011;26(9):1247-56.
10. Li H, Xie H, Woodward G. Soundscape components, perceptions, and EEG reactions in typicalmountainous urban parks. Urban Forestry & Urban Greening. 2021;64:127269.
11. Jo HI, Jeon JY. Overall environmental assessment in urban parks: Modelling audio-visual interaction witha structural equation model based on soundscape and landscape indices. Building and Environment.
2021;204:108166.
12. Xiang Y, Hedblom M, Wang S, Qiu L, Gao T. Indicator selection combining audio and visual perceptionof urban green spaces. Ecological Indicators. 2022;137:108772.
13. Haapakangas A, Hongisto V, Oliva D. Audio-visual interaction in perception of industrial plants – Effects
of sound level and the degree of visual masking by vegetation. Applied Acoustics. 2020;160.
14. Van Renterghem T, Botteldooren D. View on outdoor vegetation reduces noise annoyance for dwellers
near busy roads. Landscape and Urban Planning. 2016;148:203-15.
15. Farina A, Pieretti N. Sonic environment and vegetation structure: A methodological approach for asoundscape analysis of a Mediterranean maqui. Ecological Informatics. 2014;21:120-32.
16. Hong X, Wang G, Liu J, Lan S. Cognitive persistence of soundscape in urban parks. Sustainable Cities
and Society. 2019;51:101706.
17. Deng L, Luo H, Ma J, Huang Z, Sun L-X, Jiang M-Y, et al. Effects of integration between visual stimuli
and auditory stimuli on restorative potential and aesthetic preference in urban green spaces. Urban
Forestry & Urban Greening. 2020;53:126702.
18. Jo HI, Jeon JY. The influence of human behavioral characteristics on soundscape perception in urban
parks: Subjective and observational approaches. Landscape and Urban Planning. 2020;203:103890.
19. Song X, Lv X, Yu D, Wu Q. Spatial-temporal change analysis of plant soundscapes and their design
methods. Urban Forestry & Urban Greening. 2018;29:96-105.
20. Matsinos YG, Mazaris AD, Papadimitriou KD, Mniestris A, Hatzigiannidis G, Maioglou D, et al. Spatiotemporal variability in human and natural sounds in a rural landscape. Landscape Ecology. 2008.
21. Liu J, Kang J, Luo T, Behm H, Coppack T. Spatiotemporal variability of soundscapes in a multiple
functional urban area. Landscape and Urban Planning. 2013;115:1-9.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0145

The relationship between perception of soundscape and visitor
experience of the Great Wall Badaling Section
Xinmiao ZHU1§; Fangfang LIU1§*; Jian KANG2*; Da YANG1; Yue WU1
1

Heilongjiang Cold Region Architectural Science Key Laboratory, School of Architecture, Harbin Institute ofTechnology, Harbin, PR China
2

UCL Institute for Environmental Design and Engineering, University College London, London, United
Kingdom

ABSTRACT
It is important to preserve the soundscape and landscape of UNESCO World Cultural Heritage Sites, wherethe vision and auditory acquisition of information plays a major role in human perception. The Great Wall
Badaling section, a World Cultural Heritage Site, was selected for this study. A field questionnaire survey
with 107 respondents was carried out, aiming to examine the acoustical characteristics of the Great Wall andthe relationship between the perception of soundscape and the public visiting experience, measured by the
degree of soundscape tranquility (STD), the degree of landscape aesthetic (LAD), and the degree of visiting
satisfaction (VSD). The results indicated that: STD showed significantly negative relationship with theperceived frequency, loudness and dominance of surrounding speech. LAD was significantly positively
associated with perceived occurrences, loudness, dominance and harmonious of music sounds. And VSD
was significantly positively correlated with the perceived frequency and harmony of music and broadcastingsounds.
Keywords: Soundscape, Visiting experience, The Great Wall

1. INTRODUCTION
World cultural heritage sites have great value and significance in cultural, educational, scientificand artistic aspects, so their protection is of the utmost importance(1). The environment of a World
Heritage Site is complex and multifaceted, and require protection through a multidisciplinary
protection approach(2).
However, current World Heritage sites face high tourism pressure, rapid urbanization and theresulting growth and transformation of global cities(3). Historic heritage conservation challenges haveincreased dramatically. For the visual aspect of a World Heritage Site, the value and significance ofthe site is threatened due to the loss of visual integrity on the List of World Heritage in Danger (4). On
the other hand, the rapid development of tourism has introduced various anthropogenic sounds, suchas a large number of tourists, loudspeakers and traffic sound, which affect the experience of tourists
and reduce the cultural value of the soundscape(5, 6).
Travel experiences are complex and include visual, gustatory, tactile and sound elements(7).
Visitors' assessments of a place can be improved if soundscape and landscape are in harmony (8).
Among them, the soundscape is not only an inseparable part of the landscape, but also an important
supporting part to improve the tourist satisfaction of tourists and cultivate the local characteristics ofresidents(9, 10). Consequently, research into the protection of soundscapes at sites of world cultural
heritage is of great importance.
At the same time, the environment of the Badaling Great Wall faces natural degradation and isaffected by tourism(11). This study offers theoretical references for soundscape and landscape*Corresponding authors:

Fangfang Liu (Email: liufangfang@hit.edu.cn)
Jian Kang (Email: j.kang@ucl.ac.uk)§These authors contributed equally to this work.

protection at the Great Wall Badaling Section by exploring how soundscape perception interacts withpublic visiting experiences of the Great Wall.

2. METHODOLOGY
2.1 Study Area
The Great Wall is the most representative and largest extant cultural heritage in China, and it isalso the symbol of the Chinese spirit(11). In Chinese history, Badaling is considered the mostimportant part of the Great Wall, which lies in mountainous regions northwest of Beijing City (116°65'
E longitude, 40°25' N latitude). In 1987, the World Heritage Committee included the Great Wall on
the World Heritage List (12). We selected the scenic opening area for investigation (Fig. 1).

Figure 1 – Location of the Great Wall Badaling Section
Before a formal investigation could be conducted, a pilot study was conducted to assess the current
state of soundscapes in the Great Wall Badaling Section. According to Table 1, 15 different kinds ofsounds were recognized by participants (subjective perceptual frequency greater than 50%), which
served as the basis of the following questionnaire survey. A total of 107 valid questionnaires werecollected over six days in January, 2022.
Table 1 –the Great Wall Sound types
Sound type

CodeSound type

CodeSound type

Code

Cable car sound

CC

Gasps

GP

SingingSI

Traffic soundTS

Hawking

HW

Playing children

PC

Construction soundCS

Footsteps

FS

Bird song

BS

Music

MS

surrounding speech

SS

Wind blowing

WB

BroadcastingBR

LaughterLA

Tree rustlingTR

2.2 Questionnaire Design
2.2.1 Participant Information
In the first part of the questionnaire, participants were asked for demographic and behavioral
characteristics, such as their gender, age, education, occupation and frequency of visits.
2.2.2 Indicators of soundscape perception
Participants assessed the 15 sounds identified in the pilot study based on their previous Great Wallexperience, and performed a perceptual evaluation of the sound, namely the perceived occurrencesof the sound(POS), Perceived loudness of the sound (PLS), and preference of the sound (PRE), allusing a 5-point Likert scale(13, 14). In the function: An audible sound's dominant position depends

on how loud it is perceived and how often it occurs. According to the formula : Dominant degree ofsound (SDD) = POS*PLS. And the harmonious degree of sound (SHD) is determined by the perceived
occurrence and preference of a sound, as shown in the formula: SHD = POS*PFS.
2.2.3 Indicators of the Visiting Experience
The visiting experience is a multifaceted process that involves a number of factors, including
aesthetic and functional qualities of the landscape, as well as soundscapes(14). In order to calculate
the visiting experience, we selected the following indicators: Among these degrees are soundscapetranquility (STD), landscape aesthetics (LAD) and visiting satisfaction (VSD). Using a five-point
rating scale, all three indicators were evaluated (1=very bad to 5=very good).
2.3 Data Analysis
Participants were chosen randomly from the area. It was determined that 107 questionnaires wereeffective, including 45.8% males and 54.2 % females with normal hearing. We performed Spearman's
correlation statistical analysis using SPSS 25.0.

3. RESULTS
3.1 Soundscape Perception
The figure shows a mean value for each sound perception indicator for each source in the Great
Wall Badaling Section. The sound sources perceived by people in the Badaling section are mainly
human activities sounds (i.e., music, broadcasting, children playing, laughter, singing, gasping,hawking, footsteps, and talking), followed by the sounds produced by facilities (i.e., the sound ofcable cars, traffic and construction) and natural sounds (i.e., bird song, wind blowing and tree rustling).

Figure 2 – Sound sources in the Badaling section of the Great Wall with average perception
indicators
Most commonly perceived sound sources in POS are surrounding speech (3.63), and wind blowing(3.58). There was the highest mean value among all sounds for the PLS of the surrounding humansound (4.34), followed by laughter (3.13) and footsteps (3.00). In terms of geophysical sounds, windblowing (3.18) was the most commonly perceived. There was no discernible loudness in any of the
facility sounds, with cable car sound (1.72) getting the highest value, and construction sound (1.53),
getting the lowest. In terms of PFS, the top two natural sounds with the highest values are birdsong(3.70) and tree rustling (3.62). Laughter (3.50), Music (3.36) and singing (3.28) were the most
preferred human activity sounds. Sound from facilities such as construction (1.79), traffic (2.30) andthe cable car (2.70) received fairly low preference values.
3.2 Relationships between perception of soundscape and visiting experience
In Table 2, individual sound perception indicators are correlated with soundscape tranquility
degrees (STD). POS of surrounding speech and playing children sound showed negative relationships
with STD. PLS showed that footsteps, surrounding speech, children playing and laughter were
negatively related to tranquility. In terms of PFS, only the sound related to human activities,
specifically speech and laughter, were associated with tranquility. SDD presented similar results as
PLS. In the scenic area, the frequency and loudness of children's play sounds can be appropriately

reduced, the perceived frequency and dominance of speaking sounds, and the dominance of footsteps
can be reduced, which can create a quiet acoustic environment in the scenic area.
Soundscape aesthetic degree (LAD) is analyzed using Correlation analysis based on Spearman's
rho to determine the relationships between individual sound perception indicators (Table 2) . There
were significant relationships between POS and LAD in music and broadcasting. Only the PLS ofmusic showed a positive effect on LAD. However, PLS of footsteps and playing children soundshowed negative relationships with LAD. As for PFS, laughter and singing demonstrated positive
relationships. Playing children and music were the only significant negative and positive relationships
with LAD observed for SDD. A significant and positive relationship was found between SHD of threedifferent types of sound, including laughter, music, and broadcast. Therefore, in order to improve theaesthetics of the scenic spot, it is possible to reduce the dominance of children's playing sounds byincreasing the frequency, loudness, and harmony of the musical sound in the scenic spot, as well as
the perceived frequency of the broadcast sound.
A correlation analysis using Spearman's rho shows that sound perception indicators and visiting
satisfaction degree (VSD) are correlated (Table 2). The degree of visitor satisfaction was positively
correlated with music and broadcasting at POS. According to PLS, too much footsteps impacted thevisitor experience negatively. Three SDD indicators, including footsteps (-0.218), surrounding
speech(-0.191), and playing children(-0.229), showed statistically negative correlations with VSD. In
order to improve people's satisfaction with the visit, it is necessary to appropriately increase thefrequency and harmony of music sound, and appropriately increase the frequency, dominance andharmony of broadcast sound.
Table 2 –The relationships between perceptions of typical sound sources and visiting experience indicators
Indicator

POS

PLS

PRE

SDD

SHD

STD

SS（-.303**）

FS（-.254**）SS（.229*）

FS (-.240*）-

PC（-.231*）

SS（-.285**）LA（.220*）

SS (-.314**）PC（-.274**）

PC (-.304**）

LA（-.194*）
LAD

MS（.277**）

FS（-.210*）LA（.340**）

PC (-.205*）

LA (.244*）

BR（.219*）

PC (-.201*)SI（.202*）

MS (.261**）

MS (.273**）MS（.204*）
VSD

MS（.237*）
BR（.287**）

FS（-.235*）

BR (.230*）
FS（.195*）

FS (-.218*）

MS (.195*）

LA（.256**）

SS (-0.191*）

BR (.291**）

PC (-.229*）
BR (.238*）* (p < 0.05), ** (p < 0.01)

4. CONCLUSIONS
A study involving the Badaling section was conducted in order to determine the acousticalcharacteristics of the Great Wall and the relationship between the soundscape perception and the
public visiting experience. To summarize, STD is more prone to being affected by POS, PLS, and
SDD of surrounding speech, so controlling sound volume and frequency will be vital to maintaining
tranquility. LAD was significantly positively associated with perceived occurrences, loudness,dominance and harmonious of music sounds. And VSD was significantly positively correlated withthe perceived frequency and harmony of music and broadcasting sounds. The results show that the
Badaling Section should control a certain crowd density and appropriately increase the occurrences
and loudness of music sounds to obtain a better soundscape experience. The findings suggest thatrational control of human-related sounds is critical to the soundscape and landscape experience, andhas important implications for the Great Wall landscape and soundscape management.
Nonetheless, due to the large size and wide distribution of the World Heritage site, the Great Wallis distributed in many provinces and cities in China. Therefore, the Great Wall in each region has

different landscape and functional characteristics. In the future, different parts of the Great Wallshould be compared.

ACKNOWLEDGEMENTS
The Ministry of Science and Technology of China(grant numbers：G2021179030L) supported thisresearch. In addition, the authors would like to thank all participants in the field questionnaire.

REFERENCES
1. Perera K, Chandra D. Protection and Promotion of Cultural Heritage for Sustainable Tourism. Protection andPromotion of Cultural Heritage for Sustainable Tourism. 2011;1(1).
2. Reale D, Noviello C, Verde S, Cascini L, Terracciano G, Arena L. A multi-disciplinary approach for the damage
analysis of cultural heritage: The case study of the St. Gerlando Cathedral in Agrigento. Remote Sensing ofEnvironment. 2019;235:111464.
3. Faris Hmood K. Introductory Chapter: Heritage Conservation - Rehabilitation of Architectural and Urban
Heritage. Urban and Architectural Heritage Conservation within Sustainability. 2019.
4. Ashrafi B, Kloos M, Neugebauer C. Heritage Impact Assessment, beyond an Assessment Tool: A comparative
analysis of urban development impact on visual integrity in four UNESCO World Heritage Properties. Journalof Cultural Heritage. 2021;47:199-207.
5. He M, Li J, Li J, Chen H. A comparative study on the effect of soundscape and landscape on tourism experience.
International Journal of Tourism Research. 2019;21(1):11-22.
6. Zuo L, Zhang J, Zhang RJ, Zhang Y, Hu M, Zhuang M, et al. The Transition of Soundscapes in Tourist
Destinations from the Perspective of Residents’ Perceptions: A Case Study of the Lugu Lake Scenic Spot,Southwestern China. Sustainability. 2020;12(3):1073.
7. Agapito D, Mendes J, Valle P. Exploring the conceptualization of the sensory dimension of tourist experiences.
Journal of Destination Marketing & Management. 2013;2(2):62-73.
8. Lindquist M, Lange E, Kang J. From 3D landscape visualization to environmental simulation: The contributionof sound to the perception of virtual environments. Landscape and Urban Planning. 2016;148:216-31.
9. Kang M, Gretzel U. Effects of podcast tours on tourist experiences in a national park. Tourism Management.
2012;33(2):440-55.
10. Hong X-C, Zhu Z-P, Liu J, Geng D-H, Wang G-Y, Lan S-R. Perceived Occurrences of Soundscape Influencing
Pleasantness in Urban Forests: A Comparison of Broad-Leaved and Coniferous Forests. Sustainability.
2019;11(17):4789.
11. Chen F, Liu H, Xu H, Zhou W, Balz T, Chen P, et al. Deformation monitoring and thematic mapping of the
Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data. International
Journal of Applied Earth Observation and Geoinformation. 2021;105:102630.
12. UNESCO. The Great Wall-UNESCO World Heritage Centre.
13. Liu J, Kang J, Behm H, Luo T. Effects of landscape on soundscape perception: Soundwalks in city parks.
Landscape and Urban Planning. 2014;123:30-40.
14. Liu J, Xiong Y, Wang Y, Luo T. Soundscape effects on visiting experience in city park: A case study in Fuzhou,
China. Urban Forestry & Urban Greening. 2018;31:38-47.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0153

Influence of audio-visual interaction on the evaluation of acoustic
comfort in Taoist temples
Dongxu ZHANG1; Jun ZOU1; Yuju SUN2
1

College of Architecture and Urban Planning,Guangzhou University, China
2

Jangho Architecture,Northeastern, China

ABSTRACTThe acoustic environment of Taoist temples plays an irreplaceable role in creating a Taoist religious
atmosphere. This paper uses the research method of audio-visual evaluation experiment to find out thecharacteristics of people's comfort evaluation of the Taoist temple's acoustic environment and clarify the
influencing factors of the Taoist temple's audio-visual environment on the acoustic comfort evaluation. The
results show that audio-visual interaction will affect people's evaluation of the acoustic comfort of various
spaces in Taoist temples. The higher the matching degree of audio-visual elements, the higher theevaluation of acoustic comfort. Natural sound and Taoist music can dramatically improve the evaluation of
acoustic comfort visual conditions dominated by natural landscape elements and Taoist religious elements
respectively. The religious nature of Taoist temples will make people give a higher acoustic comfort
evaluation of Taoist music with religious characteristics. Compared with ambient sound, the overall
acoustic comfort of each space under the sound conditions of Taoist music is improved by 17.2%.
Moreover, with the increase in the proportion of Taoist religious elements in the visual landscape, theexperimenter's evaluation of the comfort of Taoist music will be significantly improved.
Keywords: Taoist temple, Acoustic comfort, Audio–visual interaction

1. INTRODUCTION
Soundscape studies were pioneered and developed by Canadian composer R.Murray Schafer ，who [1] advocated paying attention to people's auditory experience of the acoustic environment in a
specific scene environment, among which subjective acoustic comfort is an important indicator of
the soundscape evaluation. Acoustic comfort evaluation is an essential part of soundscape evaluation，Guski, R [2] indicated the different sound sources in urban landscape may also lead to different
evaluation of acoustic comfort. Meng and Kang [3] Studied the influence of social and behaviouralcharacteristics of users on their evaluation of subjective loudness and acoustic comfort. Hoffman[4]and Yang and Kang [5] indicated a slight tendency for women to be more sensitive to sound thanmen, and evidence suggests that females generally have a higher acoustic comfort than males. Geng
Y et al. [6] found that the subjective acoustic comfort vote of broadcasting music, wind & insect
sound and machine noise decreased with the increase of Universal Thermal Climate Index. Thesestudies show that acoustic comfort is not only affected by sound, but also by other factors.
With the deepening of soundscape research and the expansion of research objects, the researchmethods of acoustic comfort evaluation are also more diversified. Many scholars have studied thesoundscape by acoustical measures [7] , soundwalks [8] , interviews [9] , questionnaire surveys[10] , acoustical diaries [11] , semantic differential and Likert scales [12] , artificial neural networks[13] , etc. In consideration of 80% of human sensory experiences depend on visual stimuli [14] , theinteraction between visual landscape and soundscape has gradually become the focus of academic
attention. Hunter et al. [15] found that visual context can modulate the connectivity of the auditory
cortex with regions implicated in the generation of subjective states. This finding is an explanation
for the audiovisual interaction, in which different visual landscape reflecting varying connectivity
modulated the audio perception. Southworth [16] found that attention to visual form reduces theconscious perception of sound when aural and visual settings were coupled, and vice versa. On theimpact of visual landscapes on soundscapes, Dzhambov and Dimitrova [17] found that interaction
with green spaces had a beneficial impact on noise perception. Liu J et al. [18] revealed that visualand functional aspects should be considered in terms of creating a better soundscape during park
design and management processes. Ren Xinxin and Kang [19] found that the acoustic comforts of thesounds relating to people’s participation are increased by artificial landscape objects compared withthose under the effect of natural landscape objects, whereas natural landscape objects closely matchnatural sounds, as well as the sounds with music-related and melodic characteristics. Jeon J Y and Jo
H I [20] found that the availability of visual information affects the auditory perception of a numberof human-made and natural sounds and the availability of audio information affects the visual
perception of various visual elements. Jo Hyun In and Jeon J Y [21] found that the audio-visual
satisfaction improves when there is a higher share of water-related and greenery-related elements
than traffic-related elements. These studies all show a significant correlation between visuallandscape and soundscape evaluation, so the research of soundscape evaluation should not ignore theinfluence of visual landscape on soundscape.
The soundscape research of religious buildings has been a new research hotspot in recent years,
mainly in religious places including churches and mosques. Garrioch D [22] studied the effect ofchurch bells on the soundscapes of early modern European towns. Kang [23] measured reverberation
time and conducted questionnaire surveys to analyse how several churches in Sheffield create acomfortable acoustic environment. R. Suárez et al. [24] study the sonic characteristics of thecathedral-mosque of Córdoba and analyze the evolution of its acoustic characteristics and theiradaptation to the different liturgical needs of each period. Prawirasasra, M. S. and Mubarok, S. [25]studied the objective acoustic parameters of the Syamsul Ulum Mosque (MSU). The soundscaperesearch on Buddhist temples has also achieved some research results, Zhang D et al. [26] fieldmeasurements of the four temples’sound levels, and the representative sounds in temples wererecorded. Soundscape evaluation questionnaire surveys were distributed at the temples to identify
the subjective and objective factors of soundscape evaluation. Wang et al. [27] measured the physicalenvironment, which includes the acoustic environment, of the Tibetan Buddhist Labrang Monastery.
Soeta et al. [28] studied the effects of sound source location and direction on the acoustic fields ofJapanese Buddhist temples. Jeon et al. [29] used social surveys and soundwalks to compare thesoundscape of around a Catholic church with that of a Buddhist temple in South Korea. As for thesoundscape research of Taoist temple building. H Xie et al. [30] studied the psychological andphysiological responses of Taoist priests and ordinary people, and strategies for soundscaperenovation were investigated by conducting field measurements, interviews, soundwalks, andaudio–visual experiments. Du Qian [31] studied the physical environment such as noise, light andtemperature, analyzed the acoustic environment of Chunyang temple, and discussed the influence of
Taoist construction methods and landscaping on the acoustic environment of Chunyang temple.
This paper aims to clarify the influence of audio-visual interaction on evaluating acoustic comfort
in Taoist temples. First, choose a representative Taoist temple, and live recording to collectaudio-visual material. After that, combinatorial audio-visual materials are randomly played in thelaboratory to allow the subject to rate the comfort of the acoustic environment. The obtained
experimental results are statistically analyzed. Comparing the acoustic comfort evaluation ofdifferent auditory materials under the same visual material, and the acoustic comfort evaluation ofvarious visual materials under the same auditory material. Based on this, the influencecharacteristics of the interaction of audio-visual elements on the acoustic comfort evaluation of
Taoist temples are summarized.

2. METHODS
2.1 Survey Subjects
This paper selects three typical mountain forest Taoist temples in Liaoning Province in Northeast
China, namely Qianshan Wuliang Temple, Qianshan Wulong Temple and Jinzhou Beizhen Temple.
As the birthplace of Taoism in Northeast China, Liaoning Province has preserved many Taoist
temples with historical relic protection value. The Qianshan Wuliang Temple is famous and thelargest religious place at Beigou, Qianshan in Anshan City in Liaoning Province. The Wulong
Temple is located in the Qianshan scenic area with a beautiful environment, are people yearn for the
blessed land. Jinzhou Beizhen Temple has a long history and a strict layout, which belongs to thelarge-scale Taoist temple in Liaoning Province. These three Taoist temples played an essential roleand extensively influenced Liaoning Province and even Northeast China.
The spatial sequence of Taoist temples usually consists of several parts such as leading space,transition space, and worship space. The role of the leading space is to guide people to the temple’s
entrance gate through vegetation, streams, incense paths, memorial archways and shadow walls. The
courtyard between the entrance and the most important halls in the temple is a transitional space.
When people enter the temple dedicated to the gods through the leading space and the transition
space, they reach the climax of the Taoist architectural space sequence-the worship space.
2.2 Stimuli
Using Canon EOSM50 camera and iDAQ2022 binaural recording and playback measurement
system, audio-visual material samples of 3 key Taoist temples in Liaoning Province were recorded.
The visual material of the Taoist temple is the image taken on the spot. In order to avoid theinterference of other factors when the image is collected, it is guaranteed to be taken during a similar
period under sunny weather. The photographer maintains a wide angle and a shooting height of about
1.5 m(59 in). At the same time, in order to reflect the scene sense of the visual material and present
the real visual environment as much as possible, choose to use the 3D mode when shooting, and takethe photographer as the center, turn the direction every 60 degrees in a clockwise direction until thephotographer rotates 360 degrees stop. The selection of visual material collection locations is basedon the spatial sequence of the Taoist temple, and three types are selected: the Mountain Gate andsurrounding environment A1 (leading space), the Taoist temple courtyard A2 (transition space), andthe Taoist temple interior A3 (worship space). Among them, the leading space has a rich naturallandscape; the transition space has a good natural landscape and Taoist religious features
simultaneously; the worship space belongs to the climax space of Taoism and has strong Taoist
religious features. The images captured at each collection point constitute a set of visual materials
later produced into videos for dynamic playback. The partial images of each collection point are
shown in Table 1.
Table 1 – Visual material elements
Mountain Gate andsurrounding environment(Leading space)

Taoist temple courtyard(Transition space)

Taoist temple interior(Worship space)Note:

The natural landscape

Taoist architectural elements

While recording the visual material, the collector uses the iDAQ2022 binaural recording playback
measurement system to record the auditory material at the same location of the photographer.
Comprehensively comparing the collection points of various visual materials, it is found that thesounds in the Taoist temples include natural sounds, Taoist music, and tourists talking. In order tocontrol the variables, the ambient sounds (B1) in the three scenarios of this experiment are uniformly
selected from the audio files recorded at the Three Pagodas of Qianshan Wuliang Temple (there is noobvious Taoist ritual sound and natural sound during the recording period), and natural sound (B2)
selects the combination of birdsong and water flow, Taoist music (B3) selects the audio recorded
during the session of the Qianshan Wuliang Temple and uses ambient sound, natural sound andTaoist music as the auditory material. Then use Artemis SUITE 9.0 software to edit the experimental
auditory material, and try to keep the sound pressure level of the three auditory materials roughly thesame.
The study combined three sets of visual materials, including the Mountain Gate and surrounding
environment, courtyards and interior of Taoist temple with three collected segments of auditorymaterials involving ambient sound, natural sound and Taoist music, and then played them to simulate
field research scenarios. The combination numbers of the 9 audiovisual materials are shown in Table
2 below:
Table 2 – Audio-visual material experimental combination
Auditory materials
Visual materials

Ambient soundNatural soundTaoist music

Mountain Gate (Leading space)

A1-B1

A1-B2

A1-B3

Taoist temple courtyard (Transition space)

A2-B1

A2-B2

A2-B3

Taoist temple interior (Worship space)

A3-B1

A3-B2

A3-B3

2.3 Experiment procedure
The evaluation scale selected in the experiment is the acoustic comfort scale based on the
10-level semantic scale, and a pair of adjectives with opposite meanings, namely, “especially
uncomfortable” and “especially comfortable” are used to obtain the acoustic comfort index, “1”stands for special Uncomfortable, “10” represents particularly comfortable, and the experimenter
scores between 1 and 10 for acoustic comfort.
A total of 55 experimental participants were selected for the audio-visual experiment in this study,
including 24 males and 31 females, ranging in age from 14 to 70 years old. All experimental
participants had normal hearing and visual acuity and no color vision deficits. The experimental
participants did not engage in strenuous exercise, smoking, drinking or other behavior the day before
the experiment and had an adequate sleep. On the day of the experiment, all experimental
participants sat quietly and waited outside the lab, engaging in low-metabolic activities such as
reading and writing.
The experiment was carried out in a room 5.5 m (216 in) long, 3 m (118 in) wide, and 3.3 m (130in) high. As shown in Figure 1, “r” is the participant's seat (testing position), and “R” is theexperimenter's console. The projection screen and the Luckee C101 speaker played the visual andsound material respectively for participants, and the background noise in the room was less than 30
dB(A). About 5 minutes before the start of the experiment, let the experimenter familiarize himself
with the experimental site, the experimental process and the evaluation scale. The experimenter
calibrates the visual effect of the screen and debugs the sound material to ensure the accuracy of theaudio-visual material. After the experiment starts, the experimenters enter the laboratory in order,and for those mentioned above 9 audio-visual combination materials will appear randomly in theexperiment. Randomly play a 90s-long audiovisual material to the experimenter, and then fill in theevaluation scale within the 90s of watching the video. After this operation, the experimenter rested
for 10 minutes and repeated the previous steps as shown in Figure 2. The experiment was divided
into 3 weeks to avoid reducing the accuracy of the evaluation scale due to the experimenter’s fatigue,
watching only three audio-visual materials combinations once a week and completing the experiment
after watching the nine audio-visual materials combinations.
Rr

Figure 1 – Experimental layout

Figure 2 – Experiment procedure

3. RESULTS
3.1 The influence of auditory elements on the evaluation of acoustic comfort
Figure 3 shows the average value of the acoustic comfort evaluation of the experimenter in thesame visual material for different auditory materials. In order to analyze the influence of different
auditory materials on subjective comfort evaluation in a certain visual scene, the experimenter's
comfort evaluation of different auditory materials was grouped and entered into SPSS 22.0 software,using a single factor ANOVA analysis to conduct a significant test. The results are shown in Table 3.
acoustic comfort evaluation

The average value of the

Ambient soundNatural soundTaoist music

Mountain Gate

Courtyard

Interior

Figure 3 – The average value of acoustic comfort evaluation in different auditory materials
Table 3 – Variance analysis of acoustic comfort evaluation of different auditory materials under the samevisual material
Auditory materials
Visual materials

B1 and B2

B1 and B3

B2 and B3

Mountain Gate (Leading space)

0.034*

0.511

0.326

Taoist temple courtyard(Transition space)

0.175

0.030*

0.745

Taoist temple interior(Worship space)

0.302

0.028*

0.288

Note：* and ** represent the significance level, * represents p<0.05, and ** represents p<0.01.
When the visual material is the Mountain Gate and surrounding environment (leading space): theaverage values of the experimenter's evaluation of the acoustic comfort of ambient sound, naturalsound, and Taoist music are 6.8, 9.2, and 8.6, respectively. Compared with ambient sound, theaverage value of acoustic comfort evaluation under natural sound and Taoist music sound isincreased by 2.4 (35.3%) and 1.8 (26.5%), respectively; there is a significant difference (p<0.05)
between the acoustic comfort evaluation value of ambient sound and natural sound in the leadingspace in Table 3, not so as to the other two groups, which may indicate that setting natural sound in
the leading space will significantly improve the evaluation value of acoustic comfort, while theaddition of Taoist music in the leading space has no significant impact on acoustic comfort(compared to ambient sound).
When the visual material is the Taoist temple courtyard (transition space): the average values ofthe experimenter's evaluation of the acoustic comfort of ambient sound, natural sound, and Taoist
music sound are 8.6, 8.4, and 9.0, respectively. Compared with the ambient sound, the acoustic
comfort is reduced by 0.2 (2.3%) under the natural sound condition, and the acoustic comfort isincreased by 0.4 (4.7%) under the Taoist music sound condition; it has a dramatic difference (p<0.05)
between the acoustic comfort evaluation value of ambient sound and Taoist music sound in thetransition space in table 3, not so as to the other two groups, which may indicate that setting Taoist
music in the transition space will significantly improve the evaluation value of acoustic comfort,
while adding natural sound in the transition space has no significant impact on acoustic comfort(compared to ambient sound).
When the visual material is the Taoist temple interior (worship space): the average values of theexperimenter's evaluation of the acoustic comfort of ambient sound, natural sound, and Taoist music
are 7.8, 8.2, and 9.4, respectively. Compared with the ambient sound, the average evaluation value isincreased by 0.4 (5.1%) and 1.6 (20.5%), respectively. Compared with the ambient sound, theaverage value of acoustic comfort evaluation under the condition of natural sound and Taoist music
is increased by 0.4 (5.1%) and 1.6 (20.5%), respectively. There is a prominent difference (p<0.05)
between the acoustic comfort evaluation value of ambient sound and Taoist music sound under thecondition of worship space , not so as to the other two groups, which may indicate that setting Taoist
music sound in the worship space can significantly improve the evaluation value of acoustic comfort,
while adding natural sound in the worship space has no significant effect on acoustic comfort(compared to ambient sound).
The above results show that the highest evaluation of acoustic comfort among the three types ofspaces is the worship space under the condition of Taoist music (the evaluation of acoustic comfort
is 9.4), the leading space under the condition of natural sound (9.2), and the transition space under
the condition of Taoist music (9.0). Under the visual conditions (worship space) dominated byreligious elements, the sound of Taoist music can significantly improve the evaluation value of
acoustic comfort. When natural landscape elements dominate the visual condition (leading space),natural sound can dramatically improve the evaluation value of acoustic comfort.
3.2 The influence of visual elements on the evaluation of acoustic comfort
Figure 4 shows the average value of the acoustic comfort evaluation of the experimenter in thesame auditory material for different visual materials. To compare the influence of different visual
scenes on their subjective comfort evaluation under certain auditory conditions, the evaluation
results were entered into SPSS 22.0 software, using a single factor ANOVA analysis to conduct a
significant test. Table 4 shows the test results.
acoustic comfort evaluation

The average value of theMountain Gate
Courtyard
Interior

Ambient sound Natural sound Taoist music
Figure 4 – The average value of acoustic comfort evaluation in different visual materials

Table 4 – Variance analysis of acoustic comfort evaluation of different visual materials under the same
auditory material
visual materials
Auditory materials

A1 and A2

A1 and A3

A2 and A3

Ambient sound

0.633

0.475

0.463

Natural sound

0.841

0.757

0.463

Taoist music

0.048*

0.024*

0.022*

Figure 4 shows that when the auditory material is Taoist music, the average acoustic comfort
evaluation value in the leading space is 8.6, the transition space is 9.0 (an increase of 4.7%), and theworship space is 9.4 (an increase of 9.3%). It can be seen from Table 4 that under the condition of
Taoist music, the acoustic comfort evaluation values of the leading space, the transition space andthe worship space are all significantly different (p<0.05).
Figure 4 shows that under ambient sound conditions, the average values of the acoustic comfort
evaluation of the leading space, the transition space, and the worship space are 6.8, 8.6, and 7.8,
respectively. When the auditory material is natural sound, the average values of the acoustic comfort
evaluation of the leading space, transition space and worship space are 9.2, 8.4, and 8.2, respectively.
As the proportion of natural elements in the visual material decreases, the experimenter's evaluationof the comfort of natural sounds also decreases. However, Table 4 shows that no matter whether theauditory material is ambient sound or natural sound, there is no significant difference in theevaluation of the acoustic comfort of different visual materials.
The above results show that, compared with the ambient sound, the average value of the acoustic
comfort increases by 0.87 (12.7%) under the natural sound condition, and the average value of theacoustic comfort under the Taoist music sound condition increases by 1.27 (17.2%). Therefore, thesound of Taoist music improves people's evaluation of acoustic comfort as a whole. The possible
reason is that the religious nature of the Taoist temples will make people give a higher acoustic
comfort evaluation to music with religious characteristics such as Taoist music. With the increase in
the proportion of Taoist religious elements in visual materials (From the leading space to thetransition space to the climax space), the experimenter's evaluation of the comfort of the Taoist
music sound will be significantly improved.

4. DISCUSSION
4.1 Visual elements
Previous studies have shown that visual elements can change individual sound perception [32],and there is a close relationship between landscape and soundscape experience [18], and acoustic
evaluation is affected by visual landscape, and there are significant differences in the comfort
evaluation of the same sound under different visual landscapes [33]. The research results of thispaper verify these conclusions. The changes in visual elements significantly affect people's
perception of Taoist music. Different visual elements will produce different evaluations of acoustic
comfort under the same sound conditions. Previous research on the impact of visual material on
acoustic comfort has shown that the interaction between auditory and visual perception gives people
a sense of involvement, leading to more comfort especially when the auditory and visual components
are coherent [16]. When the matching degree of landscape elements and sound sources is high, theevaluation of acoustic comfort is relatively improved [34]. These studies are basically consistent
with the results of this paper: when the matching degree of audio-visual elements in Taoist temples ishigh, the evaluation of acoustic comfort is relatively high.
4.2 Auditory elements
Religious music directly acts on the congregation's mind and emotion to promote teachings,
create an atmosphere, enhance the experience of religious experience, and adjust the psychology of
the congregation and believers [35].It shows that the sound of Taoist music is likely to improve
people's evaluation of the acoustic comfort in Taoist temples by enhancing the sense of religious

atmosphere. In the study of Laojundong Temple, the sound of Taoist music can significantly improve
people's perception of the comfort level of the audio-visual environment , whether it is a Taoist priest
or an ordinary person[36]. It is consistent with the research in this paper, the sound of Taoist music
can significantly improve the acoustic comfort evaluation value of those visual conditions (worshipspace) dominated by religious elements. Natural sound can significantly improve the evaluation
value of acoustic comfort under visual conditions dominated by natural landscape elements (leading
space).
4.3 Suggestions for improving the acoustic environment of Taoist temples
Traditional Taoist temples are the product of Taoist beliefs. They are buildings for Taoist priests
to worship gods, practice Taoism, preach and hold prayers [37]. They are also the material carrier forstuding Chinese Taoist traditional culture. Taoist thought, with its unique wisdom, can balance therelationship between body and heart, material and spirit, and man and nature, which can still bringprofound enlightenment and spiritual comfort to the people in modern society. The acoustic
environment of the Taoist temple should be relatively quiet and independent. A good acoustic
environment is the premise to ensure the normal progress of Taoist religious rituals and theinheritance and development of Taoism.
This paper suggests that the characteristic soundscape should be preserved first. Artificial sounds
related to Taoism are the distinctive soundscapes in Taoist temples. The sounds of various
instruments, such as bells and drums, rhyme singing, scripture chanting, and lottery prayers not only
need to be fully preserved, but also moderately amplified to increase the frequency and intensity ofhearing. These sounds highlight the intensely religious atmosphere inside the Taoist temple, which
can emphasize the religious characteristics of the place and increase the comfort of the acoustic
environment. Secondly, improve the adaptability of visual landscape and sound: add natural sound tothe visual environment with a large proportion of natural landscape, relieve people's psychologicalpressure, and improve acoustic comfort. The addition of Taoist music can help to create a favorable
religious atmosphere and improve the acoustic comfort. Finally, preserving the architecturallandscape with Taoist religious characteristics, protecting the vegetation landscape around the Taoist
temple to enhance the diversity and naturalness of the natural landscape, and repairing andprotecting the buildings of the Taoist temple also have a positive effect on improving the quality of
the acoustic environment.

5. CONCLUSIONS
Through the research methods of field research, laboratory audio-visual playback andquestionnaire surveys, this paper discusses the influence of audio-visual interaction on theevaluation of acoustic comfort in Taoist temples. The specific conclusions are as follows:
1)Audio-visual interaction will affect people's evaluation of the acoustic comfort of various
spaces in Taoist temples. The higher the matching degree of audio-visual elements, the higher theevaluation of acoustic comfort. Natural sound can significantly improve the acoustic comfort
evaluation under visual conditions dominated by natural landscape elements. In the leading space of
Taoist temples, natural sound improves the acoustic comfort value by 2.4 (35.3%) compared with
ambient sound; Taoist music sound can significantly improve the acoustic comfort evaluation undervisual conditions dominated by Taoist religious elements. In the worship space, Taoist music
improves the acoustic comfort level by 1.6 (20.5%) compared with ambient sound.
2)People will give a higher acoustic comfort evaluation to Taoist music with religious
characteristics because of the religious nature of Taoist temples. Compared with the ambient sound,
the overall acoustic comfort of each space under the condition of Taoist music is increased by 1.27
(17.2%). With the increase in the proportion of Taoist religious elements in the visual material (fromthe leading space to the transition space to the climax space), the experimenter's evaluation of the
comfort of Taoist music sound will be significantly improved.
3)To improve the acoustic environment of Taoist temples, it adopts these methods, such asretaining the characteristic soundscapes in Taoist temples, heightening the adaptability of visuallandscapes and sounds, and maintaining architectural landscapes with Taoist religious
characteristics.

ACKNOWLEDGEMENTS

The authors would like to express their thanks to all the respondents who volunteered toparticipate in the research.

REFERENCES
1. Schafer R M. The tuning of the world [M]. Alfred A. Knopf, 1977.
2. Guski, R., 1997. Psychological methods for evaluating sound quality and assessing acoustic information.
Acta Acustica United with Acustica 83 (5), 765–774.
3. Meng Q, Kang J. Influence of social and behavioural characteristics of users on their evaluation ofsubjective loudness and acoustic comfort in shopping malls[J]. PloS one, 2013, 8(1): e54497.
4. Hoffman, M.L., 1977. Sex differences in empathy and related behaviors. Psychol. Bull. 84(4), 712.
5. Yang, W., Kang, J., 2005. Soundscape and sound preferences in urban squares: a case study in Sheffield.
Journal of Urban Design 10 (1), 61–80.
6. Geng Y, Hong B, Du M, et al. Combined effects of visual-acoustic-thermal comfort in campus open
spaces: A pilot study in China's cold region[J]. Building and Environment, 2022, 209: 108658.
7. Zannin P H T, Marcon C R. Objective and subjective evaluation of the acoustic comfort in classrooms[J].
Applied ergonomics, 2007, 38(5): 675-680.

8. Semidor, “Listening to a City With the Soundwalk Method”, Acta Acustica. 92, 959-964 (2006).
9. Hedfors P., Berg P.G. The sounds of two landscape settings: auditory concepts for physical planning anddesign [J]. Landscape Research, 2003, 28 (3): 245-263.
10. Brambilla G., Gallo V., Zambon G. The soundscape quality in some urban parks in Milan, Italy [J].
International Journal of Environmental Research & Public Health, 2013, (10): 2348-2369.
11. Schulte-Fortkamp B., Genuit K. The acoustical diary as an innovative tool in soundscape evaluation [J].
Journal of the Acoustical Society of America, 2004, 115 (5): 2496- 2496.
12. Kang J., Zhang M. Semantic differential analysis of the soundscape in urban open public spaces [J].
Journal of the Acoustical Society of America, 2010, 45 (1): 150-157.
13. Boes M., Oldoni D., De Coensel B., et al. Attentiondriven auditory stream segregation using a SOM
coupled with an excitatory-inhibitory ANN; proceedings of the WCCI 2012 IEEE World Congress on
Computational Intelligence, Brisbane, 2012 [C].
14. I. Rock, C.S. Harris, Vision and touch, Sci. Am. 216 (5) (1967) 96–104.
15. Hunter MD, Eickhoff SB, Pheasant RJ, Douglas MJ, Watts GR, Farrow TFD, et al.The state of
tranquility: subjective perception is shaped by contextual modulation of auditory connectivity.
NeuroImage 2010;53:611–8.
16. Southworth, M., 1969. The sonic environment of cities. Environ. Behav. 1, 49–70.
17. Dzhambov A M, Dimitrova D D. Green spaces and environmental noise perception[J]. Urban forestry& urban greening, 2015, 14(4): 1000-1008.
18. Liu J, Kang J, Luo T, et al. Landscape effects on soundscape experience in city parks[J]. Science of the
Total Environment, 2013, 454: 474-481.
19. Ren, Xinxin, and Jian Kang. "Effects of the visual landscape factors of an ecological waterscape on
acoustic comfort." Applied Acoustics 96 (2015): 171-179.
20. Jeon J Y, Jo H I. Effects of audio-visual interactions on soundscape and landscape perception and theirinfluence on satisfaction with the urban environment[J]. Building and Environment, 2020, 169: 106544.
21. Jo Hyun In and Jeon Jin Yong. Overall environmental assessment in urban parks: Modellingaudio-visual interaction with a structural equation model based on soundscape and landscape indices[J].
Building and Environment, 2021, 204.
22. Garrioch D. Sounds of the city: the soundscape of early modern Europeantowns [J]. Urban History.
2003(30): 5-25.
23. Kang J. Acoustic quality in non-acoustic public buildings[J]. Technical Acoustics ， 2006 ， 25(6):513-522.
24. R. Suárez, Sendra J J , Navarro J , et al. The sound of the cathedral-mosque of Córdoba[J].
International Journal of Sustainability in Higher Education, 2005, 6(4):307-312.
25. M,S,Prawirasasra.Evaluation of acoustical comfort in mosque[J].IOP conference series. Materials
science and engineering,2017,211(1).
26. Zhang D, Zhang M, Liu D, et al. Soundscape evaluation in han Chinese Buddhist temples[J]. AppliedAcoustics, 2016, 111: 188-197.
27. Wang T, Chen M, Hou Q. Physical environment technology of Labrang temple buildings. J Lanzhou
Jiaotong Univ 2008;27(6):45–9 (In Chinese).
28. Soeta Y, Shimokura R, Kim YH, Ohsawa T, Ito K. Measurement of acoustic characteristics of Japanese
Buddhist temples in relation to sound source location and direction. J Acoust Soc Am 2013;133(5):2699–710.
29. Jeon JY, Hwang IH, Hong JY. Soundscape evaluation in a Catholic cathedral and Buddhist temple

precincts through social surveys and soundwalks. J Acoust Soc Am 2014;135(4):1863–74.
30. Soundscape Evaluation Outside a Taoist Temple: A Case Study of Laojundong Temple in Chongqing,
China[J]. Xie Hui,Peng Zhaohui,Kang Jian,Liu Chang,Wu Huifei. International Journal ofEnvironmental Research and Public Health. 2022(8).
31. Du Qian.Site Selection of Chinese Taoist Ancient Buildings and Analysis of Acoustic and Thermal
Environment[C]//.Industrial construction 2016 SupplementⅡ.,2016:77-79+95.
32. Ismail MR. Sound preferences of the dense urban environment: soundscape of Cairo. Front Arch Res
2014;3(1):55–68.
33. Daniel T C.Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landscapeand Urban Planning，2001;54:267-281.
34. Ren, Xinxin, and Jian Kang.Interactive influence of audio-visual evaluation of wetland landscape fromthe perspective of soundscape[J]. Architectural Journal,2016，35( S2) : 7-11.
35. Li Dongdong.Psychoanalysis of the function of religious music[J].Jouranal of Panzhihua
University,2008(05):98-100.
36. Wu Huifei.Soundscape Evaluation and Sound Comfort Research of Taoist Temple in Laojundong,
Chongqing[D].Chongqing University,2019.DOI:10.27670/d.cnki.gcqdu.2019.003325.
37. Zhou Yi, et al.Taoist architecture—Shenxian Taoist Temple[M]. Beijing:China Architecture & Building
Press,2010．

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0383

Semiotic analysis of the Swedish model
for perceived soundscape quality
Jean-Dominique POLACK1
1

Institut D'Alembert, Sorbonne Université, France

ABSTRACTThe Swedish model for perceived sound quality was developed some 12 years ago by Axelsson, Nilson andBerglund as an instrument to measure the perceived affective quality of a soundscape. It is based on 8
unidirectional attribute scales, that Principal Component Analysis has shown to reduce to two axes:
Unpleasant- Pleasant on the horizontal axis; and Uneventful-Eventful on the vertical axis. Two secondary
axes are oriented in the diagonal directions: Monotonous-Exciting along the first diagonal, andCalm-Chaotic along the second one. We show that the diagonal axes build a semantic square, as proposedby Greimas in 1967, that allows a dynamical interpretation of the Swedish model. This will be proven on
the basis of our own corpus of research, which also permits the construction of a compound semiotic squarealong the horizontal axes (Unpleasant-Pleasant), confirming its actantial status as affordance.
Keywords: Soundscape quality, Semantic square, Actantial structure

1. INTRODUCTION
The Swedish model for perceived sound quality was developed by Axelsson, Nilson and Berglund[1] for evaluating existing soundscapes as well as as a guide for soundscape design. On its basis, thesame authors developed the Swedish Soundscape-Quality Protocol [2], which also includes theevaluation of visual impressions. It aims to “help non-experts (e.g., officials working formunicipalities rather than soundscape researchers) to make informed, accurate measurements ofsoundscape quality”.
In this paper, we revisit the Swedish model with the help of Greimas’s semiotic theory [3] andcomplement it with some key findings from our research group [4-8].

2. THE SWEDISH MODEL
Axelsson et al. [1] used 116 unidirectional semantic scales to evaluate 50 soundscapes in alaboratory environment. Using Principal component analysis (PCA), the main finding of Axelsson etal. [1] is that soundscape quality can be represented as a two-dimensional “circumplex” (Figure 1left) which they compare to Russell’s circumplex model of affect [9] (Figure 2 right) obtained bymultidimensional scaling (MDS) of the dissimilarities between 28 emotion labels. Beyond the factthat Russell used a free sorting task to measure dissimilarities, a technique that our group also has
used [4]), there is a striking similarity between the two panes of Figure 1 which did not escape theattention of Axelsson et al. [1].
In both panes of Figure 1, the horizontal axis is associated with Pleasure – Pleasantness for
Axelsson et al. – and the vertical axis to Arousal – Eventfulness for Axelsson et al., who carry on
explaining that “although ‘eventfulness’ is not synonymous with ‘arousing,’ it seems plausible thateventful soundscapes would be more arousing and activating than uneventful soundscapes”. But
following Russell, Axelsson et al. also introduce two alternative components, Excitement andContentment orientated at 45°, which they call Exciting and Calm after [10].

1jean-dominique.polack@sorbonne-universite.fr

Figure 1 – PCA analysis of soundscape evaluation (left, [1]); MDS analysis of emotional label (right, [10]).
More precisely, the two components of Axelsson et al.’s model correspond to the oppositions
Unpleasant-Pleasant and Uneventful-Eventful respectively; and the additional semantic scalesorientated at 45° to Monotonous-Exciting and Calm-Chaotic. Pleasant is positively correlated with
Calm and Exciting, as is Eventful with Exciting and Chaotic. The model thus obtained is presented in
Figure 2, together with the locations the 50 soundscapes of the study. Their study was later
completed by an international comparison [11] that basically confirmed the original findings, with
only small variants.

Figure 2 – The Swedish model illustrated by the 50 soundscapes [1]
The most salient difference between the two panes of Figure 1 is that most adjectives in the left
pane are gerunds (forms in -ing), while most adjectives in the right pane are past participles (forms in-ed). This is of course due to the fact that Axelsson et al. and Russell were not evaluating the some
objects – soundscapes for Axelsson et al. vs. emotions for Russell; nevertheless, it has semantic
implications that we develop in Section 4.2.

3. STRUCTURAL SEMANTICS“Language is based on oppositions”, said Saussure at the beginning of last century in his Course
in general linguistics [12]. Therefore, meaning needs the two terms of an opposition to reveal itself,
and is therefore context dependent: another meaning arises if one of the terms is changed. Here, theword “term” is taken in its mathematical sense, not in its linguistic sense. Greimas [3] expresses thesame idea more radically: “Meaning springs out of the relation between two terms in an opposition”.
For him, this is even what perception is about: “We perceives differences, and the world takes sense
in front of us and for us”.
Language cannot, therefore, be reduced to a list of labels corresponding to objects; nor to a list of
properties attached to objects, even if this list has been collectively defined by an evaluation panel.
This fact did not escaped the attention of Greimas [3] who further developed Saussure’s hypothesisthat language is based on oppositions, and derived from it a theory that explains how “meaning
springs out of the relation between two terms in an opposition”.
3.1 Elementary structure of meaning
The basic brick in Greimas’s semantic theory is the semiotic square, or elementary structure ofmeaning (Figure 3). It is constructed around an opposition between two contrary signs S 1 and S 2 on
the upper line; and is completed by introducing a new opposition between Non S 1 and Non S 2 on thelower line. Further relationships are:• contradictions between two contradictory signs on diagonal lines• implications between two complementary signs on vertical lines, orientated frombottom to top.
S 1 is arbitrarily considered positive and S 2 arbitrarily considered negative, S = (S 1 + S 2 ) is thecomplex axis, and Non S = (neither S 1 nor S 2 ) is the neutral axis. The construction of a semiotic
square proceeds from the upper opposition to the construction of Non S 1 , then to the construction of
Non S 2 , in this order.

Figure 3 – Semiotic square
3.2 Actantial model
The second brick in Greimas’s semantic theory is the actantial model (Figure 4). It was developed
from Propp’s Morphology of the Folktale [13]. It stages six actants distributed on three oppositions,each defining a specific axis:• the quest (or desire) axis opposes the subject (or hero) to the object (of the quest);• the transmission axis opposes the sender, who initiated the quest, to the receiver, whom
the quest is realized for;• the power axis opposes the helper, who helps the subject to obtain the object, to the
opponent.
axis of transmission
senderreceiverobjectaxis of desire (or quest)
helpersubject
axis of power

Figure 4 – Actantial model.

opponent

The sender is a superior authority that fixes the value of the object. This value is transmitted tothe hero, who becomes both receiver and subject, and goes on the quest. The last two actants either
help the subject to, or prevent the subject from getting the object. During the quest, the subject must
acquire qualities/skills through the qualifying test. Then follows the decisive test, where the subject
acquires the object, and the glorifying test, where the sender rewards or punishes the subject. The
three tests are integral parts of the quest.
It should be noted that actants are not the characters of a story, but correspond to structural roles
played in turn by the characters or by objects or even by concepts.

4. SEMIOTIC ANALYSIS
We now apply Greimas’s semiotic analysis of Section 3 to the Swedish model for perceived soundquality, complementing it with results from our own research.
4.1 Oppositions and semiotic squares
Figure 5 presents an illustrative example of semiotic square. Participants were asked “what is thesonorous Paris for you?” and requested to answer graphically. Analysis of their drawings [14]revealed 4 categories: 13% of the participants drew abstract schemata (top left in Figure 5), which
are idiosyncratic inasmuch as they only bear meaning for one person – the person who drew them;
20% drew maps of Paris (bottom left), a stereotype that corresponds to a learned representation
shared by all members of a given group; 23% drew organized sources (top right in Figure 5), often ina specific location as in the example, but also in generic locations; and 50% drew unorganizedsources (bottom right), side by side and without location. The total exceeds 100% since someparticipants used several strategies.

Figure 5 – Drawing the sonorous Paris (from [14]).
Figure 5 therefore opposes abstract schemata to sound sources organized in space on its top line.
The contradiction of sound sources organized in space is the map of Paris, that is, a learned abstract
representation of space without any source; and the contradiction of abstract schemata is
unorganized sound sources. Thus, without thinking of it, the investigators naturally displayed theirfinding in a semiotic square; and they intuitively organized the square so as to illustrate the dynamics
of a semiotic square [3]: rejecting individual representations (abstract schemata), perception moves
down along the first diagonal of the semiotic square to collective representations (sources), whichthen imply a shared picture of sound sources in the city, as illustrated by the thick arrows in Figure 6.
abstract schematamapspatially organized sources

unorganized sources

Figure 6 – Semiotic square for soundscape representations.

This intuitive display of research findings can also be found in the alternative components of theSwedish model for perceived soundscape quality proposed in Figure 2, which opposes Chaotic to
Exciting on its top line, and Monotonous to Calm on its bottom line, with Monotonous thecontradiction of Exciting, and Calm the contradiction of Chaotic. The corresponding semiotic squareis given in Figure 7. Here, the vertical axes imply that neglecting Monotony leads to Chaos, whereas
Excitement is obtained beyond Calm. The corresponding dynamic version is that Excitement is only
present in non-Chaotic soundscapes that have reached some Calm (thick arrows in Figure 7).
chaotic

excitingmonotonous

calm

Figure 7 – Semiotic square for the Swedish model for perceived soundscape quality.
However, all oppositions cannot be developed into semiotic squares. Indeed, diametrically
opposed semantic descriptors on the circumplex of Figure 1 often are contradictions, as signaled by
the vertical axis that oppose Eventful to Uneventful, stressing that the later descriptor is the negationof the former one. This is confirmed by [4] who, using the free sorting task used by Russell [9] tomeasure dissimilarities between 16 sound sequences, found out that the most salient difference
between them was whether they were Eventful or Amorphous: sequences were either Eventful or
Amorphous, corresponding to Uneventful in the Swedish model. This difference that was
semantically stressed by the fact that Amorphous sequences were predominantly described byphysical properties, and Eventful sequences by sound events, sources and activities: 22.1% of allverbalisations concerning Amorphous sequences relate to physical properties, and only 15.3% tosound events, sources and activities; 22.9% of verbalisations concerning Eventful sequences relate tosound events, sources and activities, and only 15.3% to physical properties.
On the other hand, the opposition between sound sources and physical properties leads to asemiotic square. Even though the contradictory terms are not expressed, they can be easily
reconstructed as no source and no property. The dynamics of a semiotic square is also functions here,
and can be advantageously compared to the cognitive approach developed in [15]: when source
identification fails, perception moves down along the first diagonal to no source, which then implies
identifying physical properties (thick arrows in Figure 8).
sources

physical properties

no property

no source

Figure 8 – Semiotic square for soundscape description.
Our last example is the display of Figure 1. As mentioned in Section 2, the most salient difference
between the two panes of Figure 1 is that most adjectives in the left pane are gerunds (forms in -ing),
while most adjectives in the right pane are past participles (forms in -ed). In other words, the displayof Figure 1 opposes two different grammatical tenses, and in fact two different grammatical voices:
the active voice where the subject of the verb is agent of the action; and the passive voice where thesubject of the verb suffers the action, that is, is patient. The contradiction at the bottom of thesemiotic square should be neither active nor passive: the middle voice where the subject of the verb
cannot be categorized as either agent or patient, having elements of both. Indeed, the middle voice
does survive lexically and syntactically in English. In other words, soundscapes are agents of theemotions, whereas the subject suffers them passively as patients. The corresponding semiotic squareis given in Figure 9 and functions at the metalinguistic level, that is, at the level that structures theorganization of knowledge [3]. Please note that both agent and patient are actants in Greimas’s sense.

agentpatient

neither patientnor agent

Figure 9 – Semiotic square for emotions.
4.2 Actantial structure
The last example hints at the actantial aspect as soundscapes. But it is best illustrated by ansoundscape experiment carried out on board of a double-decker high-speed test-train. In order tounderstand the meaning of pleasantness, Mzali [6] asked participants to list “what sounds [they had]been subjected to” during the trip, and more generally in train trips. The next questions asked to“distribute the previous sounds into 4 predefined classes: pleasant, unpleasant, annoying andindifferent sounds”. From the responses listed by the participants, 4 categories were extracted. The
distribution of the responses is presented in Figure 10, where the original French labels for theclasses are also given.
The 4 categories extracted are: general impression; vibrations and movements; events, that is,tunnels, junctions, train crossing, etc.; and sources. The percentages of responses correspond to thepercentages of participants that gave
responses
seman3cs
of belonging
annoyanceto each of the categories – the total does
not add up to 100 – and the responses are further differentiated according to the 4 predefined classes.

of all responses% de %
toutesles réponses

70

60

50

"agréable" = pleasant"désagréable" = unpleasant"gênant" = annoying"indifférent" = indifferent

40

30

20

10

0

GénéralGeneralVibrations Evénements
Vibrations
Events

Sources
SourcesMzali, 2002

Urban Sound Symposium, Ghent, April2019
Figure 10 – Semantics
of Pleasantness(adapted from [6]).
Jean-Dominique
Polack2

Figure 10 reveals that vibrations are Unpleasant, whereas events and sources are more Annoying
than Unpleasant. Specific questions, focusing on other travellers, the functioning of the train andvibrations among others, asked participants to “describe their impressions”; for doors, the question
was different: “What do you think of door noise? Describe what you are hearing? Does this noisesound unpleasant or annoying to you?” Linguistic analysis of the answers to these questions reveals
that Annoying (French Gênant) is linked to the other train users, with phrases such as “people talking
loud”, “chats” and “other travellers” listed for sources; and Unpleasant (French Désagréable) is
linked to the functioning of the train, with phrases such as “automatic doors” or “door opening(compressed air)”. In fact, doors have an ambivalent status: they become “annoying” when activatedby other train users, with phrases such as “door opening when people pass by”. This semantics ofUnpleasantness, which was confirmed by another study [16], can be deduced from the linguistic
material used to described the classes: Annoying (French Gênant) is a gerund, a verbal form that
implies an actant (see previous Section), usually a person; but Unpleasant (French Désagréable) is anadjective constructed on a verb (deverbal adjective), only implying an effect on the participant.
As a consequence of this grammatical analysis, the semantics of Pleasantness refers to actants.
First of all, we must identify the semiotic square and its fundamental opposition. The semantic
structure present some similarities with the previous cases, but is more complex as it involves 2
different semiotic squares (Figure 11). The first square opposes Pleasant to Unpleasant, with
Indifferent (neither Pleasant nor Unpleasant) being the neutral axis, that is the contradiction of bothPleasant and Unpleasant; it does not however imply any actant (no gerund). The second square
opposes Annoying to a yet undefined term, with Pleasant the contradiction of Annoying and
Unpleasant the contradiction of the undefined term. As the undefined term must also be a gerund,that is, must imply an actant, we postulate that it must be Pleasing*, the * indicating that it is notpresent in the corpus. It is however present in Axelsson et al.’s corpus [1] as Appealing. The vertical
implication axes become then evident: the first axis links Unpleasant to Annoying through an actant,
as exemplified by the doors that become annoying when someone activates them; and the second axis
links Pleasant to Pleasing*, without any concrete example in the corpus – see Figure 10. In otherwords, Unpleasantness can be considered as an Affordance [17] of doors, that is, what doors offer tothe perceiver, and doors change status to become Annoying through a qualifying test (see Section3.2): thus doors qualify as Opponent in the actant classification of Figure 4. This change of status
was developed by Fontanille in order to link the ecological approach of Gibson [17] to structural
semantics: affordance is a potentiality offered by an actant [18]. Note that the French terms Agréable
and Désagréable explicitly refer to this potentiality through the suffixes -able.
annoying

pleasing*

unpleasantpleasant(neither)indifferent

affordance level(nor)

Figure 11 – Semantics of “pleasantness” as double semiotic square.
Complete analysis of the questionnaire distributed in the test-train [6] revealed that thefunctioning of the train achieves the role of actant, that is, its sounds actively stress the travellers. On
the other side, some other sounds are just experienced by the travellers who suffer of travel sickness
or blocked ears. More precisely, sound sources are actants since they can be localized; on the other
hand the sources of low frequency vibrations, such as jolt of the whole carriage, are not localized andtherefore passively experienced by the travellers.
Needless to add, full actantial structures with the 3 tests are only obtained in full narratives suchas the ones obtained by Taupin [8,19], using collages as trigger to verbalisations. Application of thetechnique to imaginary soundscapes [20] gave the same narrative structures, with emphasis on the
Helper role of calm, nature, soft means of transportation, cultural activities such as eating out withfriends, and quick escape to recreational areas (mountain or sea). All these features are sources ofamusement, enjoyment, or simply relief and serenity, confirming once more the importance ofemotions in soundscapes.

5. CONCLUSIONS
The main message from this paper is therefore the importance of oppositions, from which“meaning springs out” [3]. The oppositions are inferred from the responses of the participants, andinclude much more semantic material than just qualifying adjectives. From these oppositions,semiotic squares can be built on any grammatical category such as nouns, verbs and adjectives, butalso grammatical tenses and voices. Also called elementary structures of meaning, semiotic squares
describe the organisation of the collective knowledge about the sounds of the world.
In the examples presented in this paper, we hope to have given the reader insights into theconceptual power of semiotic analysis of soundscapes. Among the key concepts, we want to single
out actants. As explained by Greimas [3], any object of the world, inasmuch as it is named by a
substantive or a nominal phrase, is an actant: it can be the subject or the object, in the grammaticalsense, of a sentence. Analysing the actant roles played by the objects, such as the Opponent role ofdoors in Section 4.2, helps eliciting soundscape quality by linking qualification to the major sourcesof enjoyment or annoyance, or more generally, of affects [20]. It then becomes possible, by targeting
specific actions on those specific major sources or objects, to improve soundscape quality, and evento derive guidelines for such improvement. We believe that [20] provides such an example.

ACKNOWLEDGEMENTS
The author acknowledges his debt to the groundbreaking work of the 5 doctoral candidates citedin this paper [4-8].

REFERENCES
1. Axelsson Ö, Nilsson ME Berglund B. A principal components model of soundscape perception, J.
Acoust. Soc. Am. 2010;128;2836-2846.
2. Axelsson Ö, Nilsson ME, Berglund B. The Swedish soundscape-quality protocol. J Acoust Soc Am
2012;131:3476. http://dx.doi.org/10.1121/1.4709112
3. Greimas AJ. Structural Semantics: An Attempt at a Method. Lincoln, Nebraska; University of Nebraska
Press; 1983.
4. Maffiolo V. Contribution à la caractérisation sémantique et acoustique de la qualité de l’environnement
sonore urbain. Le Mans, France; Doctoral Thesis, Université du Maine; 1999
5. Vogel C. Etude sémiotique et acoustique de l’identification des signaux sonores d’avertissement encontexte urbain. Paris, France; Doctoral Thesis, Université Pierre et Marie Curie; 1999
6. Mzali M. Perception de l’ambiance sonore et évaluation du confort acoustique dans les trains. Paris,
France; Doctoral Thesis, Université Pierre et Marie Curie; 2002
7. Guastavino C. Etude sémantique et acoustique de la perception des basses fréquences dansl’environnement sonore urbain. Paris, France; Doctoral Thesis, Université Pierre et Marie Curie; 2003
8. Taupin P. Imaginaires d’ambiance automobile et sémiotique des récits: une approche d’innovation
expérientielle digitale en Chine. Paris, France; Doctoral Thesis, Université Pierre et Marie Curie; 2017
9. Russell JA. A circumplex model of affect. J Pers Soc Psychol. 1980;39;1161-1178.
10. Cain R, Jennings P, Poxon J. The development and application of the emotional dimensions of asoundscape. Appl Acoust 2013;74:232–9. http://dx.doi.org/10. 1016/j.apacoust.2011.11.006
11. Jeon JY, Hong JY, Lavandier C, Lafon J, Axelsson Ö, Hurtig M. A cross-national comparison inassessment of urban park soundscapes in France, Korea, and Sweeden through laboratory experiments.
Appl Acoust. 2018;133;107-117.
12. de Saussure F. Course in general linguistics. Eds. Charles Bally & Albert Sechehaye. Columbia
University Press; 201113. Propp V.Morphology of the Folktale. University of Texas Press; 1968.
14. Dubois D, Vogel C, Castellengo M, Polack JD. Sound characterization of urban environment. Proc
InterNoise97; Christchurch, New Zealand 1997, p. 1239-1242
15.Dubois D, Guastavino C, Maffiolo V. A cognitive approach to soundscape. Acoustic phenomena
between “noise(s)” and “sound(s)”. Proc CFA/DAGA’04; Strasbourg, France 2004; p. 693
16. Dubois D, David S, Resche-Rigon P, Maffiolo V, Mzali M. Etude de la qualité sonore des espaces vertsde la ville de Paris. Report for the City of Paris, France; 1998
17. Gibson JJ. The ecological approach to visual perception. Lawrence Erlbaum Associates; 1986
18. Fontanille J. Pratiques sémiotiques. https://www.unilim.fr/pages_perso/jacques.fontanille/textes-pdf/
CPratiques_semiotiques2004_06.pdf
19.Taupin P. The contribution of narrative semiotics of experiential imaginary to the ideation of new digital
customer experiences. Semiotica 2019; 230;447-473. https://doi.org/10.1515/sem-2018-0005
20.Polack JD, Taupin P, Jo HI, Jeon JY. Urban Soundscapes in the Imaginaries of Native Digital Users:
Guidelines for Soundscape Design. Sustainability 2022;14(2);632. https://doi.org/10.3390/su14020632

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0436

Multiple regression model for soundscape perception in urban open
spaces based on ex-situ experiments with audios from 27 localities
Min LI1 2; Haochen GUO1 2; Rumei HAN3; Yuan ZHANG1 2 ; Jian KANG4
1
2

Eco-Building Physics Technology and Evaluation Provincial Key Lab, China

School of Architecture and Urban Planning, Shenyang Jianzhu University, China
3

4

Department of Mathematics, Shenyang Jianzhu University, China

Institute for Environmental Design and Engineering, University College London, UK

ABSTRACTSoundscape is an essential environmental element affecting people’s experience in urban open spaces. In thecomplex mechanism of soundscape perception, the acoustic characteristics of the soundscape are the primary
influencing factor. Herein, we used ISO 12913 soundscape indicators to carry out auditory perception
experiments on typical soundscape materials recorded from 27 urban open spaces. The principal indicators
pleasantness and eventfulness were calculated from the evaluation results of 68 participants. They were thentaken individually to perform multiple regression with factors including psychoacoustic, physical acoustics
indicators, the significance of various sound source types, and other indicators characterizing the soundcomposition. The results showed that: 1) The regression model for pleasantness (Ad R2= 0.703, F=21.48)
showed that the significance of bird sounds contributed positively, and the significance of mechanical sounds
and the level of S95 in the environment have a negative impact. 2) The regression model for eventfulness (AdR2=0.676, F=19.05) showed that the number of significant sound sources and the level of F50 in theenvironment contributed positively. Simultaneously, the significance of mechanical sounds has a negative
impact.
Keywords: Pleasantness, Eventfulness, Soundscape indicators

1. INTRODUCTION
Today's urban open public space is a system connected by a series of internally associated elements
and carries people's daily leisure activities. The internal elements' complexity increasingly grows withthe development of cities, especially the acoustic environment aspects. Improving people’s sense ofexperience and promoting public health seem to be the main goals of open space. Mul tiple perception
results of the acoustic environment constitute people's public space experience to a large extent.
Therefore, it is crucial to determine the perception influence factors in the environment.
As a momentous environmental element related to people’s feelings, soundscape, defined by ISO,is acoustic environment as perceived or experienced and/or understood by a person or people, incontext(1). People perceive the acoustic environment in the context and produce various experience
results, such as relieving pressure and relaxing mood under the soundscape restorative effect (2).
Pleasantness and eventfulness are two commonly used perception dimensions in soundscape study.
They originated from a circular model with pleasant on the X-axis and eventful on the Y-axis proposedby Axelson et al. (3). ISO 12913-3 formally adopts this model and gives the definition and calculation
method of them(4). Based on these two perceptual dimensions, researchers use this model to explore
the influencing factors of soundscape perception widely.
Likewise, it is also indispensable to find a soundscape indicators group suitable for urban open
public spaces to predict soundscape perception more accurately and scientifically (5). Researchersconsider the input indicators as comprehensively as possible to enhance the model's prediction ability.
The sound source temporal dynamic indicators(6), the overall perceptual indicator of the acoustic
environment from the subjective evaluation (such as the overall loudness evaluation)(7), the visual Corresponding author E-mail: y.zhang@sjzu.edu.cn

information(8), environment and context information(9), the demographic information(10).
In this study, the subjects only used auditory perception to experience and evaluate the binauralsoundscape materials from 27 urban open public spaces in an ex-situ way. According to the ISO
proposed parameter extraction method, extract the physical and psychoacoustic parameters. Establish
three sound source relevant indicators to describe their existing characteristics by jury test, including
the number of sound source types, the significance of sound sources, and the number of signif icant
sound sources. We use correlation analysis to find the potential influencing factors of pleasantness
and eventfulness and put the correlative factors into linear regression with them. Through modelingthe soundscape perceptions, we found the comprehensive influence on pleasantness and eventfulness.

2. MATERIALS AND METHOD
2.1 Soundscape materials
The soundscape binaural stimuli materials came from the SSID project ( ID: 740696, 2018030120230228), collected from 27 localities in European cities. Each material lasts for 30 seconds, and it
can recognize sounds such as traffic, construction, high-pressure water spray, stream, bird songs,
talking, footsteps, children playing, and street performances. These sounds cover the typical sounds
that may appear in most public spaces and represent their acoustic environments in different acoustic
characteristics. According to all the sound types appearing in the material, the sounds ar e classified
as human sounds (talking, footsteps, and other sounds from human activities), bird sounds, music
sounds (performance music), water sounds (stream sounds, fountain sounds, irrigation sounds), andmechanical sounds (traffic sounds, construction sounds).
2.2 Experimental design
The experiment is the second stage of SATP(11). A total of 68 subjects participated in the ex-situ
experiment. As shown in figure 1, We adopted a within-subjects design during the process in the semianechoic chamber. The experimental equipment includes a computer to control the playback of soundclips (Lenovo 310), an external sound card with headphone output function (console 6S), head phones(Sennheiser hd650), and Pads equipped with soundscape evaluation system. The headphones must be
calibrated to experience the binaurally recorded soundscapes with the same loudness found in -situ
during recording. According to the standard practice recommended by the SSID project, we use thecalibration signal SPLcalibration (1 kHz sine signal recorded at SPL 94dB) and multimeter(Fluke15b+digital) to make the headphones reach the same sound pressure level as the calibration
signal and complete the headphones' standardization. Besides, during the experiment, we used a soundlevel meter (Optimus cr:160) to ensure the background noise level could be negligible.

Figure 1 – Soundscape evaluation experiment in the semi-anechoic chamber
Before starting the actual sequence of stimuli of the experiment, arrange a stimulus to conduct anevaluation exercise for each subject. The evaluation exercise stimulus material does not belong to the
27 soundscape materials used in the formal experiment. In the formal expe riment, randomize the orderof the 27 stimuli for each participant, ask the participants to listen to the audio, and then rate thescales. There were at least 30 seconds between successive stimuli, and each participant was allowed
to re-listen to any stimulus as many times as they wished. All questions in the evaluation scale aremandatory and asked as per the ISO recommendations(12): "For each of the eight scales below, towhat extent do you agree or disagree that the present surrounding sound environment is...(translated
in Chinese)." For each of the eight attributes, use a 100-step slider, ranging from 0 (strongly disagree)
to 100 (strongly agree). The default position of the slider for the participant to start is on 50 (neutral
assessment).
2.3 Participants
The 68 subjects, all from Shenyang Architecture University, passed three preliminary screening:
1) students and teachers aged 18-55 years old, 2) no hearing impairment, and 3) healthy and able tocomplete the experiment without affecting the experimental results. Each subject filled out a Weinstein
Noise Sensitive Scale (WNSS). As shown in table 1, two participants with lower scores were excluded
by cluster analysis (systematic clustering - square Euclidean distance) through SPSS (version26, IBM,USA), so the actual compelling subjects are 66.
Table 1 – The subjects’ attributes
Gender

Age

WNSS

Male

Female

20-30

30-40

40-50

Very low-WNS

Low-WNS

High-WNS

30

38

46

8

14

2

31

35

44.12%

55.88%

67.65%

11.76%

20.59%

2.94%

45.59%

51.47%

2.4 Indicators
Referring to the analysis method of binaural data in ISO 12913-3(4), the acoustical parameters
relate to 1) physical acoustic parameter: sound pressure level A -weighted (L Aeq, dB) and thestatistical evolution of it (L 5, L50, L95 ). 2) psychoacoustic parameters: Loudness (sone), sharpness(acum), fluctuation (vacil), roughness (asper), and the statistical evolution of them(N 5, N50, N95, S, S5 ,S 95, R 10 , R 50 , F 10 , F 50). All parameters are calculated by B&K connect software. Loudness and its
statistical parameters are calculated according to the method of ISO 532 -1(13), the sharpness
parameter group's calculation follows the method described in the DIN 45692 standard (14), and theroughness and fluctuation parameter group is calculated based on the method developed by Zwicker
and Aurés(15).
Referring to the previous study that extracted and tested the indicators by jury test (9), we obtained
the sound source characteristics indicators. Including the significance degree of five sound source
types, the number of sound sources in each material, and the number of significant sound sources. The
Jury members are composed of 4 graduate students (two male and two female). The members need toanswer the following questions for the five sound source types with each soundscape material: "please
rate the significance of the five sound sources according to the audio you hear. "The answer options
are set at 0-3 points (0 point, do not hear at all. 1 point, heat a little but not dominant. 2 points,
dominates moderately. 3 points, dominates completely). The consistency test shows that there arethree sound sources' Kendall harmony coefficient W>0.8, conclude the human sounds, bird sounds,
and music sounds, which were 0.886 (p=0.000<0.01), 0.806 (p=0.000<0.01), and 0.891(p=0.000<0.01), respectively. Besides, The W of the mechanical sounds and water sounds was 0.784(p=0.000<0.01) and 0.738 (p=0.000<0.01), respectively. It shows consistency among the jury
member's evaluation results. Then calculate the mean value of the evaluation results from the fourjury members to express the significance of each sound source type. In addition, according to thenumber of sound source evaluation scores, the number of sound sources (from the number of soundsources with scores of 1, 2 and 3 in each material) and the number of sign ificant sound sources(fromthe number of sound sources with a score of 3 in each material) are calculated.
As shown in figure 2, the eight evaluation questions asked to the subjects are set according to theChinese translation of the eight soundscape attributes in ISO 12913-2(12). Meanwhile, the evaluation
results are simplified into pleasantness and eventfulness following the calculation method from
ISO12913-3(4)for subsequent analysis.
Furthermore, in view of the differences in age and occupation of the subjects, conduct a consistency
test of evaluation results among 66 subjects to ensure that the statistical analysis would not be affectedby individual differences. The results showed that for pleasantness , Kendall harmony coefficient
W=0.699, p=0.000<0.001. For eventfulness, W=0.433, p=0.000<0.001. 66 subjects were consisten t in
the evaluation of pleasantness and eventfulness. It also means that the impact of individual differences

will be lower than that of the soundscape material itself.

Figure 2 – Eight attributes from ISO in English and Chinese
2.5 Statistical analysis
This section has two parts. The first part is to conduct a correlation between the two perception
dimensions and all indicators of the soundscape characteristics to find out the potential influencing
factors related to soundscape perception. The second part establishes two regression models to findthe comprehensive influence on pleasantness and eventfulness. All statistical analyses were performed
using the software SPSS (version 26, IBM, USA). The description indicators and their symbols asshown in table 2.
Table 2 – Descriptors and symbols
SymbolDescriptors

PL

Pleasantness

EV

Eventfulness

LAeq

SPL（A-weighted, dB）

N/S/R/F

Loudness(sone)/Sharpness(acum)/Roughness(asper)/Fluctuation(vacil)

HS

Significance of Human sounds

BS

Significance of Bird sounds

MuS

Significance of Music sounds

WS

Significance of Water soundsMS

Significance of Mechanical sounds

Num

Number of sound sources

NumS

Number of significant sound sources

3. RESULTS
3.1 Correlated factors of soundscape perception
To identify which soundscape characteristics are related to pleasantness and eventfulness, a
Pearson correlation analysis was carried out. As shown in table 3, in terms of acoustic parameters,
pleasantness is correlated with L Aeq and L x, N and N x , and S95. However, for eventfulness, there is apositive correlation only with F, and F 50 shows the strongest correlation (r=0.757 ** , p=0.000).
More sound source characteristic indicators show the correlation with pleasantness. Except for theWS, all other sound source types' significance is related to pleasantness. WS shows irrelevant result
due to the diversity of the water sounds in the soundscape materials. From the materials, subjects canreceive not only natural water sounds but also the high-pressure spray sounds, fountain water sounds,
and irrigation sounds, which could bring several perceptions and are not always pleasant. For the
eventfulness, HS, MuS, and MS show the correlation. And the HS take the strongest positive
correlation (r=0.622 **, p=0.001). It may be because the human sounds are mostly produced during
activities (footsteps, conversation, children playing), which are distinguished from the non -living
bodies' sounds and easier to identify. The MS shows the weakest negative correlation (r=-0.525 **,p=0.005). It is likely because the mechanical sounds mainly come from the traffic and construction
machinery. Moreover, the traffic sounds are relatively muffled. When other sound sources are moresignificant, the traffic sounds will not feel prominent and probably be regarded as the backgroundsound of the environment by the listener. In the meantime, the construction equipment sounds always
make people feel annoying(16). Besides, the Num positively correlates with pleasantness (r=0.455 *,p=0.017 * ), and the NumS show a positive correlation with eventfulness (r=0.635 ** , p=0.000).
Table 3 – Correlation analysis for soundscape perception
Indicators

PL

EV

Pearson's rpPearson's r

p

LAeq

-0.591**

0.001

0.124

0.538

L5

-0.517**

0.006

0.195

0.329

L50

-0.573**

0.002

0.113

0.574

L95

-0.662**

0.000

0.002

0.990

N

-0.593**0.001-0.0250.903

N5-0.555**

0.003

0.156

0.439

N50

-0.627**

0.000

0.074

0.714

N95

-0.652

**

0.000

0.025

0.902

S

-0.262

0.187-0.1020.611

S5-0.168

0.402

0.027

0.894

S95

-0.540**0.004-0.3260.097

R-0.2030.311-0.075

0.711

R10

0.053

0.794

0.307

0.119

R50

-0.224

0.262

0.009

0.966

F

-0.0230.908

0.468*

0.014

F10

0.191

0.339

0.711**

0.000

F50

0.216

0.279

0.757**

0.000

Num

0.455*

0.017

0.293

0.138
0.000

NumS

0.096

0.633

0.635**

HS

0.487**

0.010

0.622**

0.001

BS

0.481*

0.011

0.076

0.706

MuS

0.453*

0.018

0.597**

0.001

WS

0.162

0.420

0.127

0.529

MS

-0.695**0.000-0.525**0.005** and * respectively indicate correlation is significant at the 0.01 level and 0.05 level (2-tailed)

3.2 Modeling soundscape perception
This section established two multiple linear regression models to find the comprehensive influence

on soundscape perception. The significant correlation variables obtained from correlation analysis
were put into regression model with pleasantness and eventfulness by stepwise regression. L 95 , S 95,
Num, HS, BS, MuS, and MS are the independent input variables for the pleasantness model by the
stepwise modeling method. The backward modeling method takes F 50, Nums, HS, MuS, and MS as
the independent input variables for the eventfulness model. Finally, two meaningful regression models
were obtained.
As shown in table 4, the most influential factor for the perception of pleasantness is MS (β=-0.540,
t=-4.663, p=0.000<0.05), with a negative effect. In addition, there are two factors that possess thenegative impact, BS (β=0.408, t=3.800, p=0.001<0.05) and S 95 (β=- 0.307, t=-2.660, p=0.014<0.05),
the three output factors explained 70.3% of pleasantness (F=21.510, p=0.000<0.05). In the dimensionof eventfulness, the model outputs three positive factors. F 50 shows the most prominent impact(β=0.514, t=2.893, p=0.008<0.05), followed by MS (β=-0.351, t=-2.971, p=0.007<0.05) and the
NumS (β=0.289, t=1.913, p=0.068<0.1), which jointly explained 67.6% of the eventfulness (F=19.047,
p=0.000<0.05).
Table 4 – Regression model of two perceptual dimensions
DV
PL

EV

IV

β

P

MS

-0.540

0.000

BS

0.408

0.001

S95

-0.307

0.014

NumS

0.289

0.068

MS

-0.351

0.007

F50

0.456

0.008

R2

F

D-W

0.703

21.510

2.042

0.676

19.047

2.312

4. CONCLUSION AND DISCUSSION
1) The L Aeq, loudness, human sounds, bird sounds, mechanical sounds, music sounds, and thenumber of sound sources are significantly correlated with pleasantness. However, water sounds havean irrelevant relationship due to the diversity of underwater sound s in the soundscape materials. The
sounds of water that can be heard in urban Spaces are not always natural, which could bring several
perceptions and are not always pleasant. In addition, fluctuation, the significance of human sounds,
mechanical sounds, music sounds, and the number of significant sound sources correlate significantly
to the eventfulness. Most mechanical sounds in urban open spaces originate from the muffled soundof traffic. Traffic sounds are not prominent compared with other types of sound sources. Listeners
probably regard them as the background sound of the environment.
2) This study's 27 binaural soundscape materials' L Aeq ave=56.94dB, std=12.87. Include common
urban open spaces such as parks, green spaces, squares, and streets. Most materials' overall acoustic
environment is quiet. In such general quiet city spaces, we established two meaningful perception
models: for the pleasantness dimension, the significance of bird sounds possesses a positive impact.
The significance of mechanical sounds and the level of S 95 in the environment has a negative impact.
For eventfulness, the number of significant sound sources and the level of F 50 in the environment havea positive impact, while the significance of mechanical sounds will bring a negative impact. The
mechanical sounds showed a considerable influence in both models, and both are negative effects.
Besides, the model's statistical evolution of psychoacoustic indicators represents the special acoustic
representation of one or some sound sources in the environment. When performing soundscapeperception prediction, this particular acoustic representation is usually recognized as backgroundsound, not a sound event, which appears in the form of acoustic parameters in the perception model.
3) Under two perception dimensions, the acoustic parameters and sound source characteristics havea particular influence. These soundscape characteristics root in the informat ion released by the soundsource, which are the physical clues provided by the environment when people perceive. The acoustic
indicators reflect the energy and frequency of the environment through numbers. People's subjective
recognition describes the sound source characteristics and delineates the acoustic environment content.
In a period acoustic environment, combining the two physical cues facilitates acoustic environment
characteristics and changes to be wholly constructed in the form of indicators and make a marvelous
soundscape perception prediction.

ACKNOWLEDGEMENTS
The authors are very grateful to Soundscape Indices (SSID) project members that helped with datacollection and technical support. This project has received funding from the European ResearchCouncil (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant
agreement No. 740696).

REFERENCES(1) International organization for standardization(2014). ISO 12913-1: acoustics soundscape part1:
definition and conceptual framework.
(2) Zhang Y, Zhang R, Zhu L. Investigation on the restoration effect of soundscape in parks in high-density
cities: Taking lu xun park in shenyang, China as an example, Universitätsbibliothek der RWTH Aachen;
2019.
(3) Axelsson Ö, Nilsson ME, Berglund B. A principal components model of soundscape perception. J
Acoust Soc Am. 2010;128(5):2836-2846.
(4) International organization for standardization(2019). ISO 12913-3: acoustics Soundscape part 3: data
analysis.
(5) Lionello M, Aletta F, Kang J. A systematic review of prediction models for the experience of urban
soundscapes. Appl. Acoust. 2020;170107479.
(6) Lavandier C, Defréville B. The contribution of sound source characteristics in the assessment of urban
soundscapes. Acta Acust united Ac. 2006;92(6):912-921.
(7) Aumond P, Can A, De Coensel B, Botteldooren D, Ribeiro C, Lavandier C. Modeling soundscapepleasantness using perceptual assessments and acoustic measurements along paths in urban context.
Acta Acust united Ac. 2017;103(3):430-443.
(8) Romero VP, Maffei L, Brambilla G, Ciaburro G. Modelling the soundscape quality of urban waterfronts
by artificial neural networks. Appl. Acoust. 2016;111121-128.
(9) Hong JY, Jeon JY. Exploring spatial relationships among soundscape variables in urban areas: A spatial
statistical modelling approach. Landscape. Urban. Plan. 2017;157352-364.
(10) Erfanian M, Mitchell A, Aletta F, Kang J. Psychological well-being and demographic factors canmediate soundscape pleasantness and eventfulness: A large sample study. J Environ Psychol.
2021;77101660.
(11) Aletta F, Oberman T, Axelsson Ö, Xie H, Zhang Y, Lau S-K, Tang S-K, Jambrošić K, De Coensel B,
Van den Bosch K. In Soundscape assessment: Towards a validated translation of perceptual attributes indifferent languages. Inter-Noise and Noise-Con Congress and Conference Proceedings, Institute of
Noise Control Engineering; 2020. p. 3137-3146.
(12) International organization for standardization(2017). ISO 12913-2: acoustics soundscape part2: datacollection and reporting requirements.
(13) International organization for standardization(2017). ISO 532-1: acoustics methods for calculating
loudness.
(14) Din D. 45692: Measurement technique for the simulation of the auditory sensation of sharpness. DIN:
Berlin, Germany, 2009.
(15) Fastl H, Zwicker E. Psychoacoustics: Facts and models, Springer Science & Business Media; 2006.
(16) Yang X, Wang Y, Zhang R, Zhang Y. Physical and psychoacoustic characteristics of typical noise on
construction site: “How does noise impact construction workers’ experience?”. Front Psychol. 2021;12.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0588

Evaluation of Psychophysiological Responses to soundscapes in nature:
Comparison of Monoscopic 360-degree panoramic video and computergenerated Virtual Reality Environment
Hyo Won YOON1; Jin Yong JEON2;
1

Department of Architectural Engineering, Hanyang University, Republic of Korea

2

Department of Architectural Engineering, Hanyang University, Republic of Korea

ABSTRACT
Participants’ subjective evaluation and psychophysiological response to audiovisual stimulation were
investigated to evaluate the soundscape recognition response to the real natural environment reproduced in
360 videos and the natural environment implemented in computer-generated virtual reality (CG VR). In orderto achieve this goal, an audio-visual environment was provided in the same head-mounted display (HMD)
device, and stimuli including various natural elements such as waterfalls, rivers, and lakes were implemented
and compared. To reproduce a space similar to the actual natural environment, the CG-VR environment iscreated by the High-Definition Render Pipeline (HDRP) technology to realize a situation in which thesubjects are staying in the natural environment. Before the experiment, individual demographic information
was investigated, and the physiological response was measured through heart rate variability, respectively,by dividing it into before, middle, and after VR stimulation for 3 minutes. In addition, after the stimulation
experience, psychological restorative response, soundscape perception response, and satisfaction were
evaluated, and the difference and relationship between psychophysiological response and subjective responseto physical stimulation were examined.
Keywords: Soundscape, Virtual Reality, psycho-physiological response

1. INTRODUCTION
With the recent emergence of virtual reality (VR) technology, various attempts have been made topromote psychophysiological restorative effects through VR environment experiences. 1,2 Overall, wedrew a common conclusion that experiences in natural environments such as forests and urban parks
positively affect human psychophysiological restoration 3. Many researchers have evaluated thepositive effects on the natural environment by representing it as 360 -degree panorama video 4 orcomputer-generated virtual reality (CG-VR) 5, but no comparison has been made in terms ofsoundscape, and studies on how audiovisual factors affect human psychophysiological restoration innatural environments are insufficient.
Therefore, this study attempted to compare the effects of forest environment experiences withvarious natural elements of virtual reality (VR) representing the real environment on emotional
restoration and autonomic nervous system response.

2. METHOD
2.1 Natural environments Development
For 360 panorama videos, six natural environments (River, Wetland, Ocean, Forest, Valley, Temple)
used in the previous study 6 were reproduced using a 360-degree camera (Insta 360 pro, Insta 360) andan Ambisonic microphone (SPS 200, Soundfield Ltd.). Four types of forest environments (Only Green,Lake, Stream, and Waterfall) were created using CG-VR technology. In addition, the environment was
created using the Unity 3D engine to create a realistic VR forest environment. To this end, Forest
Environment-Dynamic Nature from Nature Manufacture Company was used, and the High -Definition
1
2

gf@aaa.com
gf@bbb.co.jp

Render Pipeline (HDRP) setting was intended to increase immersion and realism .
2.2 Questionnaire
Prior to visiting the laboratory, we provided a psychological evaluation of individual health(Perceived Stress Scale (PSS), patient health questionnaire (PHQ-9, stay-trait inventory (STAI-Y),
and demographic information (Quality of life (WHOQOL-BREF), resilience, and noise sensitivity)
on a scale. We also evaluate the soundscape evaluation of audiovisual stimuli, the domain of visualelements (Sky, Water, Green), and auditory elements (Water, Birds) on a five -point scale.
Procedure
A total of 102 normal people were surveyed, 60 participants watched the 360 live -action videos,and 42 participants experienced the CG-VR environment. A day before the experiment, a survey was
conducted to evaluate individual demographic information and psychological status. The subject
visited a laboratory where he could experience the VR environment, and the experiment time took
about 1 hour and 40 minutes a day for 2 days. The experiment measured the physiological responsewith a Heart Rate Variability (HRV) device (SA-3000NEW (Medicore, Korea) before providing a VR
environment, and then we measured the physiological response for 3 minutes as above during the VR
natural environment experience. After HRV measurement was completed, a survey evaluation was
conducted on VR natural environment. After the subject completed the survey, a 3 -minute break time
was provided with HRV measurement, and 360 panoramic videos were repeated 6 times and CG-VR
environmental subjects 16 times, respectively.
2.3

3. RESULTS AND DISCUSSIONS
Overall subjective response in virtual natural environments
In this study, analysis was conducted using SPSS Statistics (IBM, version 27). Psychologicalrestorative responses to nature were evaluated through the Shorter-version Revised Restorative Scale(SRRS) survey, and eight items and visual, auditory, and overall satisfaction with the naturalenvironment were analyzed through t-test to compare responses to the two VR environment types.
3.1

Table 1. Differences between 360 Panorama video and CG-VR in subjective responses(* p <0.05, ** p <0.01)

Response

Type
Mean
Std
t
Sig
360 Panorama
6.78
1.90
Natured-2.79
0.006**
CG-VR
7.27
1.20
360 Panorama
6.73
2.06
Relaxed-4.32
0.000**
CG-VR
7.55
1.33
360 Panorama
1.59
1.08
Out of breath
4.01
0.000**
CG-VR
1.21
0.57
360 Panorama
1.52
1.05
Sweaty4.38
0.000**
CG-VR
1.14
0.36
360 Panorama
5.64
2.09
Exciting-2.90
0.004**
CG-VR
6.28
1.92
360 Panorama
6.29
1.80
Concentration-2.11
0.036*
CG-VR
6.70
1.66
360 Panorama
6.08
2.08
Want to visit
-1.20
0.233
CG-VR
6.35
2.02
360 Panorama
6.20
2.07
Want to stay
-0.71
0.476
CG-VR
6.36
2.07
360 Panorama
3.89
0.81
Acoustic-0.7670.444
satisfaction
CG-VR
3.97
0.99
360 Panorama
4.18
0.71
Visual1.477
0.141
aesthetic
CG-VR
4.05
0.85
360 Panorama
4.09
0.69
Overall
0.773
0.440
impression
CG-VR
4.02
0.80
Through this, it was confirmed that the evaluation of the CG-VR environment was statisticallysignificantly higher than that of the 360 Panorama video in six of the psychological restorative surveys,

and there was no significant difference in satisfaction with the VR environment.
Differences in physiological responses to the natural environments
The overall physiological response changes of subjects were confirmed through experiments, andSDNN, TSI (Temporal Stationary Index), TP (Total Power), Low Frequency (LF), High Frequency(HF), and LF/HF Ratio indicators were analyzed through HRV measuring equipment. As a result, after
stress stimulation in the two VR environments, it increased and decreased statistically significantly in
the SDNN and TSI indicators, respectively. In medical terms, SDNN means stress resistance and TSI
shows stress index, so it was confirmed that VR natural environment has a restorative effect inphysiological response as shown in Figure 1 below, and the 360 Panorama video showed a higher
3.2

restorative response while appreciating VR environment than CG-VR.

Figure 1. Differences between 360 Panorama video and CG-VR in HRV responses( *** p <0.001)

4. CONCLUSIONSIn this study, people's psychophysiological and perceived audiovisual responses to the naturalenvironment were investigated through natural environments in VR experience. In terms of physiology,
the effectiveness of the restorative effect was confirmed to be superior to that of CG-VR, but on thecontrary, CG-VR was confirmed to have a high restorative response from a psychological perspective,
and both groups were restored from a virtual natural environment experience. These findings are
expected to be used as useful basic data when promoting emotional and psychological restorative
through VR experiences or designing natural spaces in the future.

ACKNOWLEDGEMENTS
This research was supported by a grant from the National Research Foundation of Korea (NRF),
funded by the Korean government (MSIT) (No. 2020R1A2C2009716). This study is a summary of thecontent of the paper that is submitted to the conference. It is stated that copyrights of all content,
figures, and tables in this manuscript are owned by the conference.

REFERENCES
1. Annerstedt, Matilda, et al. Inducing physiological stress recovery with sounds of nature in a virtual reality
forest—Results from a pilot study. Physiology & behavior 118 (2013).
2. Gao, Tian, et al. "Exploring psychophysiological restoration and individual preference in the different
envi-ronments based on virtual reality." International journal of environmental research and public health16.17 (2019).
3. Jeon, Jin Yong, Hyun In Jo, and Kounseok Lee. Potential restorative effects of urban soundscapes: Personality traits, temperament, and perceptions of VR urban environments. Landscape and Urban Plan-ning
214 (2021).
4. Ratcliffe, Eleanor. "Sound and soundscape in restorative natural environments: A narrative literature
review." Frontiers in Psychology 12 (2021).

5. Annerstedt, Matilda, et al. "Inducing physiological stress recovery with sounds of nature in a virtual
reality forest—Results from a pilot study." Physiology & behavior 118 (2013).

6. Jo, Hyun In, Kounseok Lee, and Jin Yong Jeon. "Effect of noise sensitivity on psychophysiologicalresponse through monoscopic 360 video and stereoscopic sound environment experience: arandomized control trial." Scientific Reports 12.1 (2022).

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0593

Emotional restorative effect of combined contents of Korean
traditional music with natural soundscapeHaram LEE1; Jin Yong JEON2
1

Department of Architectural Engineering, Hanyang University, Republic of Korea

2

Department of Architectural Engineering, Hanyang University, Republic of Korea

ABSTRACTThis study was conducted to confirm whether the combined content of Korean natural soundscape andtraditional music had a positive psychophysiological effect on the emotions of modern people. To accomplish
the goal of this study longitudinal experiment was carried out and in the process of the experiment, visualelements of soundscape content were provided in the CAVE environment, and auditory elements and music
were provided through headphones to investigate the responses of participants. In providing combined
content, traditional Korean music was classified and presented according to the tempo, genre, musicalinstrument group, and perceived feelings of song, and in the case of soundscape images, it was classified andpresented according to the ratio of vegetation, sky, water (river, sea, valley), and artifact. Physiologicalresponses were investigated through electroencephalogram (EEG) and heart rate variability (HRV).
Participants were general adults with no visual or hearing problems, and the effect was confirmed bycomparing psychophysiological responses before and after the experience. To confirm the causality of theeffect, six-week longitudinal experiment was planned.
Keywords: Soundscape, Restoration effect, psychophysiological response

1. INTRODUCTION
The psychophysiological restoration effect induced by the experience of the audio-visualenvironment is confirmed through psychological and physiological responses. In terms of
psychological response, positive effects such as stress reduction and positive emotional reinforcementof positive soundscapes have been confirmed based on the questionnaire and subjective evaluation(Krzywicka & Byrka, 2017; Medvedev et al., 2015; Shu & Ma, 2020). In addition, considerable
research on the restorative effect of psychological aspects of natural space has been conducted, andcognitive function (Van Dijk-Wesselius, Mass, Hovinga, Van Vugt, & Van den Berg, 2018; Shu & Ma,
2019;), stress Positive effects such as recovery and positive emotional reinforcement (Aletta et al.,2018; Medvedev, Shepherd, & Hautus, 2015; Choe, Jorgensen, & Sheffield, 2020) ha ve been
demonstrated. In terms of physiological response, positive physiological response changes due tonatural soundscape have been confirmed using various physiological response indicators such as heartrate, heart rate variability, skin conductance level, electroencephalography, etc. (Yu et al., 2018;
Medvedev et al., 2015; Hume & Ahtamad, 2013; Li & Kang, 2019; Deng et al., 2020; Li et al., 2021).
In particular, Li & Kang (2019) found that a soundscape containing natural sound reduced heat rate,
respiratory frequency, and respiratory depth and increased α and β waves through comprehensive
physiological indicators investigation, but only weak degree of relationship has been revealed.
However, studies related to soundscape restoration are still very scarce (Aletta, Oberman, & Kang,
2018), and there is a limitation that a general conclusion could not be reached because each researcher
showed different results. In addition, it is necessary to investigate the exposure time through temporal
data analysis approach. Nevertheless, in the case of EEG, it does not stop at the level of PSD (power
spectrum density) analysis at the sensor level, but an approach through high -level response analysis
12

haramchi@hanyang.ac.kr
jyjeon@hanyang.ac.kr

according to brain regions through the analysis of indicators such as no de power and connectivity at
the source level. As the psychophysiological restoration effect of the soundscape experience wasconfirmed through the analysis of HRV and EEG indicators, research and discussion on it arecontinuing.
Recently, due to the development of virtual reality technology, studies have been conducted toconfirm the psychophysiological restoration effect through VR environment experience (Annerstedt
et al., 2013; Gao et al., 2019; Mattila et al., 2020; Jeon et al., 2021; Yu et al., 2018). Furthermore,potential recovery effects through urban environment experiences as well as natural soundscapes havebeen reported (Jeon et al., 2021). This goes beyond a simple possibility, and speaks of the possibility
that it can be a long-term effect of the emotional restoration. In addition, an attempt to apply thisrestoration effect is also in progress. In this study, Korean traditional music, Gugak, was combinedwith Korean nature soundscape content to investigate how Koreans with relatively high leve ls of stresswere affected.

2. METHOD
Soundscape video contents were recorded in high quality using a 6 -channel 360 camera (Insta 360
pro, Insta 360), and the sound was recorded using a 4-channel ambisonic microphone (Soundfield SPS
200, Sound field Ltd.) with a portable sound recorder (Mixpre-6, Sounddevices) and recorded with an
A-formant first-order ambisonic set-up. In addition, when establishing an evaluation environment ina laboratory environment, simultaneous measurements were performed using a separa te half-inch
microphone (AE 46, GRAS Sound and Vibration) to calibrate to the same sound pressure level as theactual field, and portable sound It was connected to a recorder (AS-70, RION) and recorded. In thelaboratory environment, the subjects experienced the visual environment through a curved CAVE
environment (with 3 beam projectors) and received auditory stimulation through a closed -type
headphone (HD-450, Sennheiser). This experiment was an initial experiment, and 5 graduate students
in their mid-20s participated. A questionnaire and physiological response measurement (HRV, EEG)
were conducted before and after the 15-minute combined contents experience. The total process took
about an hour.
HRV was measured using SA-3000NEW (Medicore, Korea). At this time, a total of 4 parameters
were selected as the HRV measurement index, and the definition of the selected indices are as follows:
1) total power (TP), which means the overall activity level of the autonomic nervous system, 2) SDNN,
the standard deviation of normal to normal, which means the stress resistance, 3) Temporary
stationarity index (TSI), which expresses the degree of pressure applied to the human body usingparameters such as HR and HRV, and means a stress index 4) heart rate (HR), which means the averageheart rate for 1 minute. EEG measurement was performed using a DSI-24 instrument. In order to see
the degree of EEG activation, power spectral density analysis was performed and to see local
efficiency and global efficiency, network analysis was also performed.

3. RESULTS AND DISCUSSIONSThe physiological response due to combined content experience was analyzed by HRV and EEG.
The results of HRV response are shown in Fig. 1(a), and the Alpha/Beta ratio (A/B ratio) value of
EEG is shown in Fig. 1(b). The HRV responses are as follows. In terms of TP, the values of all 5
subjects increased by more than 10%. In the case of SDNN, it increased by more than 5% for all 5
subjects. In the case of TSI, which indicates the degree of stress, all tended to decr ease, and HR also
showed a tendency to decrease also. Based on the HRV responses, it can be confirmed that theautonomic nervous system of the subjects was activated and that there was a physiological positiveeffect according to SDNN and TSI. In particular, the improvement in the aspect of stress can be
mentioned. Also, since HR decreased, it can be interpreted as physiological stability.
As a result of analyzing the power spectral density of the EEG response, it can be confirmed that
the A/B ratio of all subjects increased by more than 10%. Since the ratio of the alpha wave, whichmeans a stable state, and the beta wave, which means an excited state, is increased, it is possible tosay that the subjects became physiologically stable.

Figure 1. (a) HRV responses, (b) Alpha/Beta ratio
The results of network analysis based on the subjects' EEG responses are shown in Fig. 2. Althougha certain trend was not confirmed here, it can be concluded that the contents experience had a positiveeffect since the local efficiency or global efficiency of all subjects increased.

Figure 2. (a) Local efficiency, (b) Global efficiency

4. CONCLUSIONS
Through the physiological responses confirmed in this experiment, the possibility of restoration
effect of contents combining Korean natural soundscape and Gugak was confirmed. Similar to theprevious studies, a cross-sectional experiment was conducted, but the causality could be confirmed
only when this experiment was conducted as a longitudinal experiment for more than ab out 6 weeks.
In addition, it is thought that if the experiment with more subjects is conducted, a definite tendency
and influence can be confirmed. Also, it is necessary to analyze the interaction between thesoundscape contents and of Gugak. In the case of EEG analysis, it is necessary to divide regions andanalyze each region, and it is also necessary to find a brain network that has a high correlation withthe restoration effect.

ACKNOWLEDGEMENTS
This research was supported by a grant from the National Research Foundation of Korea (NRF),
funded by the Korean government (MSIT) (No. 2020R1A2C2009716). This study is a summary of thecontent of the paper that is submitted to the journal. It is stated that copyrights of all content, figures
and tables in this manuscript are owned by the journal.

REFERENCES
1. Krzywicka, P., & Byrka, K. (2017). Restorative qualities of and preference for natural and urban
soundscapes. Frontiers in psychology, 8, 1705.
2. Medvedev, O., Shepherd, D., & Hautus, M. J. (2015). The restorative potential of soundscapes: Aphysiological investigation. Applied Acoustics, 96, 20-26.
3. Shu, S., & Ma, H. (2020). Restorative effects of urban park soundscapes on children’s
psychophysiological stress. Applied Acoustics, 164, 107293.
4. Van Dijk-Wesselius, J. E., Maas, J., Hovinga, D., Van Vugt, M. V. D. B. A., & Van den Berg, A. E. (2018).
The impact of greening schoolyards on the appreciation, and physical, cognitive and social-emotional
well-being of schoolchildren: A prospective intervention study. Landscape and urban planning, 180, 1526.
5. Shu, S., & Ma, H. (2019). Restorative effects of classroom soundscapes on children’s cognitive
performance. International journal of environmental research and public health, 16(2), 293.
6. Aletta, F., Oberman, T., & Kang, J. (2018). Associations between positive health-related effects andsoundscapes perceptual constructs: A systematic review. International journal of environmental researchand public health, 15(11), 2392.
7. Medvedev, O., Shepherd, D., & Hautus, M. J. (2015). The restorative potential of soundscapes: A
physiological investigation. Applied Acoustics, 96, 20-26.
8. Choe, E. Y., Jorgensen, A., & Sheffield, D. (2020). Does a natural environment enhance the effectivenessof Mindfulness-Based Stress Reduction (MBSR)? Examining the mental health and wellbeing, and natureconnectedness benefits. Landscape and Urban Planning, 202, 103886.
9. Yu, C. P., Lee, H. Y., & Luo, X. Y. (2018). The effect of virtual reality forest and urban environments on
physiological and psychological responses. Urban forestry & urban greening, 35, 106-114.
10. Hume, K., & Ahtamad, M. (2013). Physiological responses to and subjective estimates of soundscapeelements. Applied Acoustics, 74(2), 275-281.
11. Li, Z., & Kang, J. (2019). Sensitivity analysis of changes in human physiological indicators observed insoundscapes. Landscape and Urban Planning, 190, 103593.
12. Deng, L., Luo, H., Ma, J., Huang, Z., Sun, L. X., Jiang, M. Y., ... & Li, X. (2020). Effects of integration
between visual stimuli and auditory stimuli on restorative potential and aesthetic preference in urban
green spaces. Urban Forestry & Urban Greening, 53, 126702.
13. Li, Z., Ba, M., & Kang, J. (2021). Effect of soundscape on physiological indicators of audio-visualinteractions in urban environments. Sustainable Cities and Society, 103360.
14. Annerstedt, M., Jönsson, P., Wallergård, M., Johansson, G., Karlson, B., Grahn, P., ... & Währborg, P.
(2013). Inducing physiological stress recovery with sounds of nature in a virtual reality forest—Resultsfrom a pilot study. Physiology & behavior, 118, 240-250.
15. Gao, T., Zhang, T., Zhu, L., Gao, Y., & Qiu, L. (2019). Exploring psychophysiological restoration andindividual preference in the different environments based on virtual reality. International journal ofenvironmental research and public health, 16(17), 3102.
16. Mattila, O., Korhonen, A., Pöyry, E., Hauru, K., Holopainen, J., & Parvinen, P. (2020). Restoration in a
virtual reality forest environment. Computers in Human Behavior, 107, 106295.
17. Jeon, J. Y., Jo, H. I., & Lee, K. (2021). Potential restorative effects of urban soundscapes: Personality
traits, temperament, and perceptions of VR urban environments. Landscape and Urban Planning, 214,
104188.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0603

A benchmark comparison of perceptual models for soundscapes ona large-scale augmented soundscape dataset
Kenneth OOI; Karn N. WATCHARASUPAT; Bhan LAM; Zhen-Ting ONG; Woon-Seng GAN
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

ABSTRACT
Psychoacoustic indicators and spectrogram-based features are standard inputs to perceptual models for soundscape analysis. However, existing models in the soundscape literature are trained on different collections of
input parameters and frequently mutually exclusive datasets, which complicates fair comparisons of model
performance and generalizability to unseen data and soundscapes. Hence, we use the ARAUS dataset, alarge-scale, publicly-available dataset of 25,440 responses to unique augmented soundscapes, as a common
benchmark for comparison of the relative performance of a selection of input and model types used in previous soundscape studies, as well as deep neural network architectures commonly used for other acoustic
tasks. The different model types were used in a regression task to predict “Eventfulness” ratings (as definedin ISO/TS 12913-3) given by participants as responses to the augmented soundscapes. Subsequently, we
compared their performance as classification models for the classification of soundscapes into the quadrants
generated by the Pleasantness-Eventfulness axes of the ISO/TS 12913-3 circumplex model. The five-foldcross-validation set of 25,200 responses and an independent test set of 240 responses making up the ARAUS
dataset was used to facilitate unbiased comparisons.
Keywords: Soundscape augmentation, dataset, regression, classification, deep neural networks

1.

INTRODUCTION

Cognizant of the fact that the international standard for soundscapes (in ISO 12913-1:2014) defines a“soundscape” to be “an acoustic environment as perceived or experienced and/or understood by a person or
people, in context” (1), soundscape studies have striven to analyze and model soundscapes using perceptual
indicators on top of, or in lieu of, standard decibel-based indicators used in noise control (2). In line withthe shift towards perception-based methods for noise control, soundscape augmentation techniques, which
involve the introduction of wanted sounds (otherwise known as “maskers”), have been widely explored anddeveloped in the literature. These techniques have been effective in improving or changing perceptual indicators of existing soundscapes, such as the subjectively-rated restorativeness (3), perceived loudness (4),pleasantness (5), satisfaction (6), and annoyance (7). This is despite the overall sound pressure level increasing due to masker addition, which corroborates findings that purely reducing sound pressure levels may notnecessarily result in effective noise mitigation (8).
Soundscape augmentation techniques can be applied in laboratory (9), virtual-reality (10) mixed-reality(11), or in-situ conditions (12), with diverse types of maskers. For example, previous studies have investigated
the use of natural sounds like birdsong (13) and water sounds, as well as music (14) and artificially-generated
sounds (15), as maskers to improve the overall perception of a given soundscape. However, not all naturalsounds necessarily improve the perception of a given soundscape (16), and perceptual changes caused by theaddition of music can also be polarizing depending on the nature of the source, as can be seen by a study by
Guastavino (17), where live music from musicians was generally perceived positively whereas recorded musicfrom loudspeakers or car radios was generally perceived negatively. Furthermore, soundscapes augmented
with human voice have been perceived to be more chaotic and disruptive (18), and in Jeon et al. (19), it canbe seen that when construction noise was added to traffic noise, the resulting annoyance ratings could exceed
1 {wooi002, karn001, bhanlam, ztong, ewsgan} @ ntu.edu.sg

those of the individual noise types even when the combined sound pressure levels were equal to those of theindividual noises.
Hence, when given a set or selection of possible maskers that could be augmented to an existing soundscape, picking the best one(s) that change perceptual indicators in a desired manner when added to the existingsoundscape may not be trivial. To reduce the need for tedious human evaluation for every novel scenario,
perceptual soundscape models trained on a wide variety of augmented soundscapes could be used to predict
perceptual parameters of soundscapes before and after introduction of a given masker, and the choice of thebest masker(s) could be made based on the masker(s) causing the greatest change in parameter values. Alternatively, the task could be reformulated as a classification task, where the best masker(s) are chosen based onwhether they are able to change the predicted class of a given soundscape after augmentation. The main aimof this study is therefore to train a selection of linear models and deep neural networks as regression and classification models that could be used for this purpose, using a large-scale dataset of responses to augmented
urban soundscapes.

2.

DATASET

The dataset we used to train and compare models for the present study is the ARAUS (Affective Responses
to Augmented Urban Soundscapes) dataset (20). The ARAUS dataset is publicly available at https://doi.
org/10.21979/N9/9OTEVX, and full details on its design and data collection process can be found at https://doi.org/10.48550/arXiv.2207.01078. Hence, for this section, we provide a brief overview of of the ARAUS
dataset, and elaborate upon highlights that have not been previously discussed.
2.1 Overview
The ARAUS dataset is made up of 25 440 human responses to augmented urban soundscapes presented
as audio-visual stimuli, with responses collected using parts 2 and 4 of the Method A questionnaire in ISO
12913-2:2018 (21). The audio-visual stimuli were generated using 120 binaural recordings from the Urban
Soundscapes of the World (USotW) database (22) as “base” urban soundscapes, and 280 source tracks under
Creative Commons licenses from the Freesound1 (FS) and Xeno-canto2 (XC) libraries as “maskers” used
to augment the USotW soundscapes. The maskers were chosen to fit into five pre-determined classes (80bird, 40 construction, 40 traffic, 80 water, and 40 wind), drawing from similar classes of sounds previously
investigated in the soundscape literature. Since the source tracks from Freesound and Xeno-canto had varying
lengths, sampling frequencies, and numbers of channels, they were processed to single-channel tracks with astandard length of 30 s at a sampling frequency of 44.1 kHz.
The ARAUS dataset is also partitioned into a cross-validation set with five independent folds (each with
5040 responses), and an independent test set (with 240 responses). Hence, four of the folds can be used fortraining and the fifth can be used for validation of models, which allows for an 80/20 train-validation split ofthe dataset typical in machine learning scenarios. The test set can then be used to perform a final evaluationof model performance.
2.2 Fold Allocation
To ensure that the folds in the cross-validation set of the ARAUS dataset were truly independent, the baseurban soundscapes and maskers were allocated to the five folds such that the sets of soundscapes and maskers
in each fold were disjoint. This was done via the same procedure for the set of soundscapes, as well as thesets of maskers in each class.
Firstly, a group of summary statistics for a selection of standard acoustic and psychoacoustic indicators
were computed for each recording in a given set (of soundscapes, or maskers from each class). The computation was performed separately for each channel of the recording, and resulted in a total of 134 summary
statistics per channel.
Secondly, a principal component analysis (PCA) was performed on the summary statistics, and only
enough principal components were extracted such that 90% of the observed variance was explained. This
resulted in a total of 9, 13, 9, 8, 8, and 6 principal components being extracted for the sets of soundscape,bird masker, construction masker, traffic masker, water masker, and wind masker recordings.
Notably, the number of summary statistics computed for each set of recordings also outnumbered thenumber of recordings in the sets themselves. For example, 132 summary statistics were computed for theset of 80 bird masker recordings as input features for PCA. While this may be an invalid use of PCA from
1 Accessible at https://freesound.org/.
2 Accessible at https://xeno-canto.org/.

a factor-analytic point of view, the purpose of the application of PCA in the fold assignment was purely
for noise and dimensionality reduction, and we did not attempt to interpret any of the principal components
resulting from the transformation. This makes the use of PCA here valid, because there are no assumptions
on the number of input features when PCA is used purely for dimension reduction. This has previously
been illustrated by Bergner (23), who summarized a multitude of acoustic/psychoacoustic parameters into
principal components via PCA, even though the number of input features (over 150 features) far exceeded
the number of recordings (27 recordings).
Thirdly, representing each recording as coordinates in the truncated principal component space, a selforganizing map (SOM) was used to divide each set of recordings was divided into clusters of five (24). A
2-dimensional, 10-by-10 lattice of neurons was used for the SOM, with an initial learning rate of 1 for the
self-organizing phase and 0.1 for the convergence phase.

10

Principal component 2

Principal component 2

15
5
0
5
10

5
0
5
10

10
0
10
10

10

0
20
Principal component 1
Principal component 2

Principal component 2

20

15

5
0
5
Principal component 1

5

0
5
10
Principal component 1

15

5
0
5
Principal component 1

10

10
0
10

10

10

20

Principal component 2

Principal component 2

15

10
0
10

10
5
0
5

10

0
10
Principal component 1

20

10

0
10
Principal component 1

Figure 1: Distribution of soundscape (top left), bird masker (top right), construction masker (middle left),
traffic masker (middle right), water masker (bottom left), and wind masker (bottom right) recordings after
fold allocation, represented as points projected to the space generated by the first two principal componentsof their corresponding PCAs. Different marker types denote recordings allocated to different folds, with blue
dots, red circles, black pluses, green crosses, and purple triangles denoting folds 1, 2, 3, 4, and 5, respectively.

Lastly, each recording in a cluster of five was assigned randomly to a distinct fold. This allowed a more
uniform distribution of recordings in terms of the principal component spaces generated by their acoustic andpsychoacoustic parameters. Coupled with the density-matching property of the SOM, the recordings for eachfold also cover similar areas in the principal component spaces, as can be observed from Figure 1.

3.

MODELSFor this study, we formulated two regression tasks and one classification task based on the responses
obtained in the ARAUS dataset for which to compare different types of models. The two regression tasks
required models to predict the normalized “ISO Pleasantness” and “ISO Eventfulness” of a given augmented
soundscape in the ARAUS dataset, as defined in ISO/TS 12913-3:2019 (25), and models were evaluatedaccording to the mean squared error (MSE) in prediction. The classification task required models to classify soundscapes into one of the four quadrants generated by the orthogonal “ISO Pleasantness” and “ISOEventfulness” axes in the ISO/TS 12913-3:2019 circumplex model of soundscape perception, and models
were evaluated according to their classification accuracy. We compared the performance of dummy models
learning only from the ground-truth labels, linear models (standard linear regression, lasso regression, ridge
regression, and elastic net regression), and a convolutional neural network (CNN) on these tasks. This section
describes the individual tasks and types of models used.
Using a five-fold cross-validation scheme, each model type was trained five times, each with a different
fold of the cross-validation set used as the validation set (5040 samples) and the remaining four folds usedas the training set (20 160 samples). The CNNs were sensitive to random initialization, so results from 10
differently-seeded runs per validation fold were recorded. After training, each model was evaluated on the
independent test set (48 samples).
3.1 Tasks
3.1.1 Regression
To define the regression tasks, let rpl , rev , rch , rvi , rue , rca , ran , rmo ∈ {1, 2, 3, 4, 5} be the responses to the 8questions in part 2 of the Method A questionnaire in ISO/TS 12913-2:2018 regarding the extent to which thesound environment in a given stimulus was respectively pleasant, eventful, chaotic, vibrant, uneventful, calm,
annoying, and monotonous. Furthermore, let √2 rpl − ran + 2 (rca − rch + rvi − rmo )

,
(1)
P=
√ 
8 1+ 2√2 (rev − rue ) + 2 (rch − rca + rvi − rmo )

E=
(2)
√ 
8 1+ 2
respectively be the normalized values of “ISO Pleasantness” and “ISO Eventfulness” as defined in ISO/TS
12913-3:2019 (25), where P, E ∈ [−1, 1] by their definition. The regression tasks require the prediction of thevalues of P and E for each stimulus, with the ground-truth (i.e., “correct”) labels computed using the actual
responses provided by the participant rating that stimulus. In addition, the “ISO Pleasantness” regression task
has been discussed in (20) for the ARAUS dataset, but with a different set of models for comparison.
3.1.2 Classification
For the classification task, we consider the ISO/TS 12913-3:2019 circumplex model of soundscape perception, as shown in Figure 2. The circumplex model has the ISO Pleasantness P and ISO Eventfulness
E as main orthogonal axes, with descriptors “Vibrant”, “Chaotic”, “Calm”, and “Monotonous” located at
45°-angles to the main axes. In this manner, they are also located in the respective quadrants (going counterclockwise) generated by the P and E axes and we use them as textual labels for the quadrants while coding
them numerically as classes 0, 1, 2, and 3.
Therefore, the classification task requires the prediction of a value Q ∈ {0, 1, 2, 3} for each stimulus, withthe ground-truth labels for Q computed as a function of the ground-truth labels for P and E provided by theparticipant rating that stimulus. Rigorously speaking, we define

𝑬

𝑷

Figure 2: ISO 12913-3:2019 circumplex model of soundscape perception with textual descriptors for eachquadrant drawn from (25), and their corresponding integer-valued classes.


0,



1,
Q(P, E) =

2,



3,

if P > 0 and E > 0
if P ≤ 0 and E > 0
if P ≤ 0 and E ≤ 0
if P > 0 and E ≤ 0(3)

to be the ground-truth label for the classification task, where P and E are respectively defined by Equation (1)and Equation (2).
3.2 Model Types
3.2.1 Dummy Models
The dummy regression models were trained to predict the same value of ISO Pleasantness (or ISO Eventfulness) regardless of the augmented soundscape: the mean value of the ground-truth ISO Pleasantness (orISO Eventfulness) labels in their training sets. Similarly, the dummy classification models were trained to
predict the same class label regardless of the augmented soundscape: the mode of the ground-truth quadrantlabels in their training sets.
These are extremely naive methods of prediction because the models learn solely from the ground-truthlabels and disregard the input features completely. Nonetheless, we investigated their performance as a sanity
check for all the other models in this study, since any model that successfully uses meaningful information to
make predictions necessarily performs better than the dummy models.
3.2.2 Linear Models
The linear models were firstly trained as regression models to predict the ISO Pleasantness P and ISO
Eventfulness E of an augmented soundscape using its acoustic and psychoacoustic indicators as input feabbtures. Their respective regression predictions
P and E for P and E were then used to generate the predictedb Eb , where Q is as defined in Equation (3).
quadrant for the classification task as Q P,
Moreover, the acoustic and psychoacoustic indicators used as input features were exactly the channel-wisesummary statistics detailed in Section 2.2 for each binaural augmented soundscape in the ARAUS dataset,
which resulted in the linear models using 264 possible input features for regression. The linear models were
all trained with a regularized MSE loss functionn L (xx, y ; w ) = ∑ (yi − wi xi )2 + α |wi | + β w2i ,

(4)

i=1



where n is the number of training samples, y = y1 · · · yn are the ground-truthlabels(for ISO Pleasant

x
·
·
·
x
ness or ISO Eventfulness,
dependingonthedesiredmodel
output),x=
arethe input features,n1w = w1 · · · wn are the weights for each input feature (i.e., the parameters or coefficients of the regression
model) to be trained, and α, β ≥ 0 are regularization parameters.
We varied the values of α and β to get the following four linear models:20000
8434
3557
1500
632
267
112
47
20
0.0

0

Intensity (dBFS)

Frequency (Hz)

1. A standard linear regression model with α = β = 0 (i.e., no regularization).
2. A lasso regression model with α = 1 and β = 0 (i.e., with L1 regularization only).
3. A ridge regression model with α = 0 and β = 1 (i.e., with L2 regularization only).
4. An elastic net model with α = 12 and β = 41 (i.e., with both L1 and L2 regularization).
3.2.3 Convolutional Neural Networks
Separate CNNs were trained to predict the ISO Pleasantness, ISO Eventfulness, and quadrant label of anaugmented soundscape using its log-mel spectrograms as input. The parameters used to compute the log-mel
spectrograms were identical to those used in (20), and an example of a log-mel spectrogram obtained froman augmented soundscape in the ARAUS dataset is shown in Figure 3.
The log-mel spectrograms, which were represented as 644 × 64 × 2 tensors, were subsequently used as
input to the CNN models with general model architecture shown in Figure 4. The architecture is identical tothe baseline model architecture used for the acoustic scene classification task (Task 1B) of the DCASE 2020
Challenge (26), but with different output layers. For the regression models predicting the ISO Pleasantness
and ISO Eventfulness, the output layer was a dense layer with a single neuron and linear activation. For
the classification models predicting the quadrant label, the output layer was a dense layer with four neurons(corresponding to each of the four possible classes) and softmax activation. The CNN models each had about
140 000 trainable parameters.

20
40
60
3.0

6.0

9.0

12.0

15.0

Time (s)

18.0

21.0

24.0

27.0

30.0

×
×

×

×

×

×

×

Figure 3: Log-mel spectrogram of the left channel of stimulus #11 shown to participant #00001 in the ARAUS
dataset. The stimulus was generated by adding the first 30 s of R0079 (New York, Central Park - The Pond)from the USotW databse with a 30-second excerpt of XC109203 (Black Rail) at an SMR of −6 dB.
.

Figure 4: General architecture for the CNN models used for regression and classification, adapted from (26).

4.

RESULTS AND DISCUSSION

Table 1 and Table 2 respectively compare the results of the regression models for ISO Pleasantness andEventfulness, as evaluated by their MSEs in prediction. Table 3 compares the results of the classification
models for the classification of soundscapes into the four quadrants shown in Figure 2, as evaluated by theirclassification accuracy.

Table 1: Mean squared errors (± standard deviation over 10 differently-seeded runs where applicable) of ISO
Pleasantness models for the regression task outlined in Section 3.1.1. Smaller values are desirable, as denotedby the down arrow (↓).
Mean squared error (↓)
Model
Dummy
Linear regression
Lasso regression
Ridge regression
Elastic net
Convolutional neural network

Train

Validation

Test

0.15510.11690.14290.11800.13510.1152 ± 0.0020

0.15530.14130.14320.14080.13570.1212 ± 0.0010

0.1192
0.52850.1016
0.48680.09300.0865 ± 0.0063

Table 2: Mean squared errors (± standard deviation over 10 differently-seeded runs where applicable) of
ISO Eventfulness models for the regression task outlined in Section 3.1.1. Smaller values are desirable, as
denoted by the down arrow (↓).
Mean squared error (↓)
Model
Dummy
Linear regression
Lasso regression
Ridge regression
Elastic net
Convolutional neural network

Train

Validation

Test

0.16310.11400.14440.11450.13630.1193 ± 0.0013

0.16330.12920.14490.12500.13710.1181 ± 0.0006

0.0971
0.06390.06530.0701
0.0442
0.0619 ± 0.0039

Table 3: Accuracy (± standard deviation over 10 differently-seeded runs where applicable) of quadrant classification models for the classification task outlined in Section 3.1.2. Larger values are desirable, as denotedby the up arrow (↑).
Accuracy (↑)
Model
Dummy
Linear regression
Lasso regression
Ridge regression
Elastic net
Convolutional neural network

Train

Validation

Test

0.3207
0.48370.41940.48140.43630.4859 ± 0.0039

0.32070.44230.41820.44090.43380.4739 ± 0.0028

0.31250.29170.52920.30420.64170.5896 ± 0.0117

Most of the linear and CNN models performed better than the dummy models in the training, validation,and test sets for all the tasks, which suggests that the features used in all the baseline models to make predictions of the ISO Pleasantness, ISO Eventfulness, and circumplex quadrant were indeed meaningful. The only
exceptions were in the performances on the test set for the standard linear regression and ridge regression
models in the regression task for ISO Pleasantness and the classification task. The standard linear regression
models had a test set MSE of 0.5285 for prediction of ISO Pleasantness and the ridge regression models had
a test set MSE of 0.4868, which were both worse than that of the dummy models at 0.1192. Likewise, thestandard linear regression models had a test set accuracy of 0.2917 and the ridge regression models had a test
set accuracy of 0.3042, which were both worse than that of the dummy models at 0.3125.
This indicates that the standard linear regression and ridge regression models may have been overfitted,
and may not be suitable for use as prediction models for soundscape augmentation when using the acoustic

and psychoacoustic indicators in Section 2.2 as input features.
On the other hand, the best performing models in terms of validation and test set MSE were the elastic
net and CNN models. In particular, the CNN models performed the best among all models in this study
in terms of validation set MSE for the ISO Pleasantness regression task (0.1212), the test set MSE for the
ISO Pleasantness regression task (0.0865), the validation set MSE for the ISO Eventfulness regression task
(0.1181), and the validation set accuracy for the quadrant classification task (0.4739). The elastic net models
performed the best among all models in this study in terms of test set MSE for the ISO Eventfulness regression
task (0.0442) and validation set accuracy for the quadrant classification task (0.6417).
This was as expected for the CNN models due to spectrograms being analogous to grayscale images andthe success of CNN models in both visual and acoustic tasks outside the realm of soundscape augmentation,and similarly for the elastic net models due to the loss function in Equation (4) causing most coefficients of
the regression model (represented as wi in Equation (4)) to be set to 0 after training concludes (27). This
indirectly allows elastic net models to automatically choose suitable parameters for regression from an initiallarger selection, such as the 264 possible input features described in Section 3.2.2.
Incidentally, the exact predictions made by the elastic net models for the regression tasks wereT 
+0.30351−7.8731 · 10−3  Nmax Pb = 
−3
−0.4512 · 10  M10 kHz  ,M12.5 kHz−1.7326 · 10−3T 
−0.31481
+1.0097 · 10−2  Nmax  +0.2682 · 10−3  LA,max  Eb = 
−0.8885 · 10−3  M5 Hz  , −0.2461 · 10−3  M6.3 Hz 
M630 Hz+0.8199 · 10−3

(5)

(6)

where Nmax is the maximum loudness (in sone), LA,max is the maximum A-weighted sound pressure level(in dB), M f is the power (in dB) at the one-third octave band with center frequency f , Pb is the prediction
for the normalized ISO Pleasantness value P, and Eb is the prediction for the normalized ISO Eventfulness
value E. This implies that the most salient features for ISO Pleasantness models in the context of soundscapeaugmentation are likely to be the peak loudness and high-frequency components, whereas the corresponding
features for ISO Eventfulness models in the context of soundscape augmentation are likely to be the peak
loudness and sound pressure level, as well as the low- to mid-frequency components.
Lastly, the dummy models for ISO Pleasantness had lower MSEs than the dummy models for ISO Eventfulness for the training (0.1551 against 0.1631) and validation (0.1553 against 0.1633) sets, but not for thetest (0.1192 against 0.0971) sets. Nonetheless, the other models had relatively similar performance in the
training and validation sets as both ISO Pleasantness and ISO Eventfulness models. Since the training andvalidation sets had significantly greater numbers of samples than the test sets, this signifies that while there
are larger inherent variations in ISO Eventfulness ratings in the five-fold cross-validation set of the ARAUS
dataset as compared to the ISO Pleasantness ratings, the input features allowed models to predict both values
in an equally reliable manner.

5.

CONCLUSION

In conclusion, we trained a variety of models to perform regression and classification tasks using the
ARAUS dataset. The regression tasks included the prediction of ISO Pleasantness and ISO Eventfulness
values associated with each augmented urban soundscape in the ARAUS dataset, and the classification task
involved the classification of each augmented urban soundscape into the quadrants where the descriptors “Vibrant”, “Chaotic”, “Calm”, and “Monotonous” are located in the ISO 12913 circumplex model of soundscapeperception. The best-performing models for all the tasks were elastic net models (with less than 10 parameters) using acoustic and psychoacoustic indicators as input features, and convolutional neural networks (withmore than 100 000 parameters) using log-mel spectrograms as input features.
Future work could involve the validation of the models in actual in situ soundscape augmentation contexts,
where maskers from the ARAUS dataset could be picked to maximize or minimize the values of ISO Pleasantness and ISO Eventfulness for soundscapes outside of the ARAUS dataset. Alternatively, other modeltypes could be investigated for their efficacy in the selfsame tasks, such as random forest models, fuzzy logic
models, or other deep neural network architectures.

ACKNOWLEDGEMENTS
This research is supported by the Singapore Ministry of National Development and the National Research
Foundation, Prime Minister’s Office under the Cities of Tomorrow Research Programme (Award No. COTV4-2020-1). Any opinions, findings and conclusions or recommendations expressed in this material arethose of the authors and do not reflect the view of National Research Foundation, Singapore, and Ministry of
National Development, Singapore.

REFERENCES
1.

International Organization for Standardization. ISO 12913-1:2014 - Acoustics - Soundscape - Part 1:
Definition and conceptual framework. Geneva, Switzerland: International Organization for Standardization; 2014.
2. Lionello M, Aletta F, Kang J. A systematic review of prediction models for the experience of urban
soundscapes. Applied Acoustics. 2020;170:107479.
3. Masullo M, Maffei L, Pascale A, Senese VP, De Stefano S, Chau CK. Effects of Evocative Audio-Visual
Installations on the Restorativeness in Urban Parks. MDPI Sustainability. 2021;13(15).
4. Fang X, Gao T, Hedblom M, Xu N, Xiang Y, Hu M, et al. Soundscape Perceptions and Preferences forDifferent Groups of Users in Urban Recreational Forest Parks. MDPI Forests. 2021;12(4):468.
5. Puyana-Romero V, Maffei L, Brambilla G, Nuñez-Solano D. Sound water masking to match a waterfront soundscape with the users’ expectations: The case study of the seafront in Naples, Italy. MDPI
Sustainability. 2021;13(1):1-20.
6. Ozcevik Bilen A, Yuksel Can Z. An applied soundscape approach for acoustic evaluation – compatibility
with ISO 12913. Applied Acoustics. 2021;180:108112.
7. Cai J, Liu B, Liu J, Yu N. Masking effect of natural water sound on the typical industrial noise. In: ICSV2008. International Institute of Acoustics and Vibration, IIAV; 2018. p. 3515-21.
8. Kang J, Aletta F, Oberman T, Erfanian M, Kachlicka M, Lionello M, et al. Towards soundscape indices.
In: 23rd International Congress on Acoustics; 2019. p. 2488-95.
9. De Coensel B, Vanwetswinkel S, Botteldooren D. Effects of natural sounds on the perception of roadtraffic noise. JASA Express Letters. 2011;129(4):148-53.
10. Lugten M, Karacaoglu M, White K. A VR experiment testing the effects of fountain sound and visible
vegetation on soundscape quality in areas exposed to aircraft noise. In: Inter-Noise 2017. Hong Kong;
2017. .
11. Hong JY, Lam B, Ong ZT, Ooi K, Gan WS, Kang J, et al. A mixed-reality approach to soundscapeassessment of outdoor urban environments augmented with natural sounds. Building and Environment.
2021;194:107688.
12. Van Renterghem T, Vanhecke K, Filipan K, Sun K, De Pessemier T, De Coensel B, et al. Interactive
soundscape augmentation by natural sounds in a noise polluted urban park. Landscape and Urban Planning. 2020;194:103705.
13. Ferraro DM, Miller ZD, Ferguson LA, Taff BD, Barber JR, Newman P, et al. The phantom chorus: birdsong boosts human well-being in protected areas. Proceedings of the Royal Society B.
2020;287:20201811.
14. Mathiesen SL, Aadal L, Uldbæk ML, Astrup P, Byrne DV, Wang QJ. Music Is Served: How Acoustic
Interventions in Hospital Dining Environments Can Improve Patient Mealtime Wellbeing. MDPI Foods.
2021 10;10(11).
15. Regazzi R, Cunha B, de Miranda HV, Acosta JJG, Barbosa CRH, Frota MN, et al. Development andvalidation of a masking system for mitigation of low-frequency audible noise from electrical substations.
Applied Sciences (Switzerland). 2021;11(17).
16. Ratcliffe E. Sound and Soundscape in Restorative Natural Environments: A Narrative Literature Review.
Frontiers in Psychology. 2021;12:570563.
17. Guastavino C. The Ideal Urban Soundscape: Investigating the Sound Quality of French Cities. Acta
Acustica United with Acustica. 2006;92:945-51.
18. De Bruijn M. The effect of added sounds on urban soundscape evaluation and listening activity; 2021.
Available from: https://theses.liacs.nl/1920.

19. Jeon JY, Lee PJ, You J, Kang J. The evaluation of urban soundscape with combined noise sources andthe effect of water sounds. In: Inter-Noise 2009; 2009. p. 4042-6.
20. Ooi K, Ong ZT, Watcharasupat KN, Lam B, Hong JY, Gan WS. ARAUS: A Large-Scale Dataset andBaseline Models of Affective Responses to Augmented Urban Soundscapes; 2022. Available from:https://arxiv.org/abs/2207.01078v2.
21. International Organization for Standardization. ISO 12913-2 Acoustics - Soundscape - Part 2: Data
collection and reporting requirements. Geneva, Switzerland: International Organization for Standardization; 2018.
22. De Coensel B, Sun K, Botteldooren D. Urban Soundscapes of the World: Selection and reproduction ofurban acoustic environments with soundscape in mind. In: Proceedings of Inter-Noise 2017; 2017. .
23. Bergner J, Preihs S, Peissig J. Soundscape Fingerprinting - Methods and Parameters for Acoustic Assessment. Fortschritte der Akustik - DAGA. 2021;(October).
24. Kohonen T. Self-organizing maps. Springer-Verlag Berlin Heidelberg; 2001.
25. International Organization for Standardization. ISO 12913-3:2019 - Acoustics - Soundscape - Part 3:
Data analysis. Geneva, Switzerland: International Organization for Standardization; 2019.
26. Heittola T, Mesaros A, Virtanen T. Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions. In: Proceedings of DCASE 2020 Workshop. Tokyo,
Japan; 2020. Available from: http://arxiv.org/abs/2005.14623.
27. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal StatisticalSociety Series B: Statistical Methodology. 2005;67(5):768.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0937

The Soundscape of the Sarek National Park – Case Study
Dorota CZOPEK1; Sandra LICHON2; Pawel Małecki3, Jerzy WICIAK4
1, 2, 3

AGH University of Science and Technology, Poland

ABSTRACTThe paper presents an analysis of the soundscape of the oldest national park in Europe – the Sarek National
Park in Lapland in northern Sweden. In the summer of 2020, a scientific expedition took place during which
acoustic measurements, ambisonic recordings and 360° video recordings were made. The characteristic
places in different parts of the valley were selected for the analysis. The paper presents the results of theanalysis of recorded data using classical method and the soundscape method. Basic classical acoustic
parameters used in environmental acoustics were calculated. Listening tests (soundwalk) were performed
with the use of virtual reality tools. In addition, psychoacoustic parameters and soundecology indicators suchas: loudness, sharpness, or roughness, ACI (acoustic complexity index), NDSI (normalized difference
soundscape index), BI (bioacoustic index), ADI (acoustic diversity index), AEI (acoustic evenness index)
were used. The applied analysis allowed for the comparison of multi-temporal acoustic patterns of different
parts of valley and their comparison with the subjective assessment of the listeners.
Keywords: VR technolgy, Lapland, UNESCO

1. INTRODUCTION
The development of urbanization in recent years has significantly changed the landscape of various
areas. Anthropogenic sounds usually dominate in these urbanized areas, thus noise in cities has
become a fundamental and annoying problem and also reduces the quality of life. Hence, many people
are looking for peace and relaxation in green areas, especially in the National Parks. One of the mostbeautiful lands and one of the few completely deserted areas where the biggest predators live is the
Sarek National Park in Lapland in Sveden. It is an inaccessible and desolate mountain area, stretchingamong rivers, lakes and glaciers, it is recommended only to experienced and properly equipped
tourists. Registration of acoustic environments allows you to capture their phonic richness and uniquefeatures, which are constantly exposed to all kinds of changes directly related to human factors [1,3,5].
The paper presents the results of the analysis of recorded data in the Sarek National Park. The
analysis was performed using classical method and the soundscape method. Basic classical acoustic
parameters used in environmental acoustics were calculated. Listening tests (soundwalk) were
performed with the use of virtual reality tools [7-9]. In addition, psychoacoustic parameters andsoundecology indicators such as: loudness, sharpness or roughness, ACI (acoustic complexity index),
NDSI (normalized difference soundscape index), BI (bioacoustic index), ADI (acoustic diversity
index), AEI (acoustic evenness index) were used [2]. The applied analysis allowed for the comparisonof multi-temporal acoustic patterns of different parts of valley and their comparison with thesubjective assessment of the listeners.

2. LAPLAND AND THE SAREK NATIONAL PARK
Lapland covers the northern parts of the Scandinavian Peninsula and the Kola Peninsula today. It
is located in 4 countries: Russia and three Nordic countries: Finland, Sweden and Norway. It covers
an area of approx. 390 thousand square meters. km². Most of the territory of Lapland (Sápmi) lies tothe north of the Arctic Circle. The western part is an area of fjords, deep valleys, glaciers and1

dorota.czopek@agh.edu.pl
lichol@student.agh.edu.pl3
pawel.malecki@agh.edu.pl4
jerzy.wiciak@agh.ed.pl2mountains, with the highest peak of Kebnekaise (2,111 m) in Swedish Lapland. The Swedish part ofLapland is characterized by large rivers flowing from the northwest to the southeast.
Sarek National Park is located in northern Sweden about one hundred kilometers beyond the Arctic
Circle and covers an area of 1,970 square kilometers. It was established in 1909. In December 1996,
the neighboring national parks: Sarek, Stora Sjöfallet (1,278 sq km) and Padjelanta (1984 sq km) wereentered on the UNESCO World Heritage List. Sarek NP is characterized by an alpine landscape withmagnificent mountain ranges, narrow valleys, glaciers, swift rivers with numerous rapids andwaterfalls. There are six of Sweden’s thirteen highest mountains, almost 100 glaciers and long, deepand narrow valleys. Also the national park is known for its wildlife, including large elk and many
predators. Meltwater from the many glaciers flows into Sarek’s artery, the Ráhpaaädno river, which
creates the alpine world’s most well-known delta. There is no wide range of flora species in the park.
This is mainly due to the fact that most of the park, with the exception of the southern and southeastern parts, is above the coniferous growth limit (approx. 500 m). There are no tourist facilities in
the park area, no marked trails, no shelters where you can spend the night indoors. The area bears thestamp of the inland ice sheet, and a hike through Sarek is a trip through Sweden’s geological history.

Figure 1 – Sarek National Park – location on the Scandinavian Peninsula and topographic map
Measurements and recording of sound landscapes were carried out in 10 places located between:
Aktse asnd Skarja. Among which, the soundscapes captured in three places of the Sarek National Park:
Renvaktarstuga, Tjasskávárvásj, Skárjá were analyzed further.
The soundscapes used to recreate and simulate the natural environment with very few
anthropogenic sounds. The main sound source at the first location- Renvaktarstuga is wind. There arealso noticeable rustling of grass and faint sounds of singing birds. The acoustic environment
documented at the site called Tjasskávárvásj is phonically very similar to the first studied landscape– the wind is also the main source of sound. There are, however, much more audible biophonic sounds
made by birds with quite high frequencies. The soundscape of Skárja is the most diverse. The main
source of sound is the sound of the water. You can also hear the wind, the rustling of the grass and thesounds of human origin. There are also soft sounds of singing birds in the soundscape.

Figure 2 – The view from the measuring point in Renvaktarstuga and Tjasskávárvásj

Figure 3 – The view from the measuring point in Skárjá

2 METHODS AND MEASUREMENT
The analysis of the recorded soundscapes was carried out using the method of recording theacoustic environment and later recreating it in a laboratory with a sound system and using VR
technology [4,6]. Environmental recordings were made using an ambisonic microphone, 360° cameraand sound analyser SVAN957. Laboratory tests were carried out using a virtual reality set - Oculus
Quest 2. Due to the different formats of data recording, the use of the virtual reality set required:- conversion of the recorded image,- synchronization of spatial image and sound,- rendering,- calibration of the playback level.
Calibration was carried out in an anechoic chamber with the use of the HATS (Head and Torso
Simulator) simulator with built-in microphones in the ears by Bruel & Kjaer ½” type 4189 and theDAW – Pro Tools software.

Figure 4 – Calibration of the playback level in an anechoic chamber
The sound level was the same – 30 dBA for the Renvaktarstugi and Tjasskávárvásji soundscapes.
For the third soundscape used in simulations, the sound pressure level was 60 dBA.
The psychoacoustic evaluation of the soundscape was carried out with the use of questionnaires.
The questions concerned the issues of loudness assessment, general impression, matching the soundto the environment, describing emotions experienced at a given moment, but also specifying theexisting sound sources. There were questions verifying the context and considering the extent to which
the studied environment is perceived as monotonous, dynamic, varied, unvaried, irritating, pleasant,calm or chaotic. In the questionnaires, a five-point Likert scale was used, assigning five statements tothe possible responses regarding the degree of compliance with the examined feature. The survey
included 8 questions and basic personal information. Psychoacoustic research was carried out on agroup of 30 people using a questionnaire that was completed by each participant after simulating thesoundscape in the auralization laboratory. In total, each examined person completed the questionnaire
three times. The study participants included 13 men and 17 women. The group of respondents waslimited only to students of the AGH University of Science and Technology Stanisław Staszic inKrakow. The age of the respondents ranged from 21 to 23 years old, the largest part of which were22-year-old.
The research was carried out in the auralization laboratory of the Department of Mechanics andVibroacoustics of the AGH University of Science and Technology in Krakow. The examined person
sat on a swivel chair that allowed him to turn freely during the simulation. Then, the test participant
put on the virtual reality goggles - Oculus Quest 2 and received one of the two controllers compatible
with the VR headset. The controller allowed each person to set individual characteristics for eachplayed file. The controller also made it possible to navigate in cyberspace and to turn on subsequent
movies. The playback levels were calibrated individually for each recording so that the test participant
would perceive the same sound level as was actually present in the given place.
The examined person was presented three spherical films with spatial sound depicting threedifferent sound landscapes. Each simulation lasted three minutes and was completed with a survey.
The movies were played in the following order: Renvaktarstuga, Tjasskávárvásj, Skárjá.e the same
sound level that was actually present in the given place.

Figure 5 The auralization laboratory during the psychoacoustic evaluation of the soundscape

3 Results
Tables 1 and 2 present selected results of the survey, respectively comparison of the overall
assessment of the acoustic environment of the three tested places with the mean and standard deviation,and the responses regarding the reception of the test site as pleasant, irritating, calm and chaotic.
Figure 1 presents a graphical classification of the examined soundscapes
Table 1 - Comparison of the adjustment of sound to the environment for the three tested places
How does the sound fit in with the environment? [%]Place
Perfect
Very good Moderately
Renvaktarstuga
53
44
3
Tjasskávárvásj
50
40
10
Skárjá
23
64
13

A little
000

Not at all000

MeasureMean
St. dev.
4,5
0,57
4,4
0,68
4,1
0,61

None of the analyzed places was assessed by the respondents as little or not suited to thesurroundings. In a minority, some of the respondents assessed the sound matching in each tested place
as moderate. By far, most found matching soundscapes to be very good or perfect.
The analysis of the results presented in Table 2 shows that none of the respondents described thestudied acoustic environments as definitely unpleasant. The soundscapes were also not rated as rather
unpleasant. Only 10% of the study participants characterized Skárja's soundscape as rather unpleasant
The first landscape Renvaktarstuga was definitely the most pleasantly perceived, the average valueof which was 4.7. In this comparison, the acoustic environment of Skárja was the worst – the averagescore was 3.97. Overall, all of the soundscapes were found to be rather or overwhelmingly enjoyable.
The next question of the survey questionnaire examined the extent to which the analyzed
soundscape are perceived as irritating. The soundscape of Renvaktarstuga was rated 100% as
definitely non-irritating. None of the three locations was identified as being clearly irritating. The
third soundscape Skárjá was found to be the most annoying place. The mean scores for all soundscapes

ranged from 1 - 1.87, which means that the respondents defined a small degree of compliance withthe examined statement.
Table 2 - Comparison of the acoustic environment loudness assessment of the three tested places
Do you agree with the statement that the presented acoustic environment is ... [%]Place
Definitely
Rather Neither yes,
RatherDefinitely
yes
yes
nor nonotnopleasant
Renvaktarstuga 70
30
000
Tjasskávárvásj 37
50
13
0
0
Skárjá
37
46
7
10
0
irritating
Renvaktarstuga 0
0
0
0
100
Tjasskávárvásj 0
0
27
27
46
Skárjá
0
13
13
20
54
calm
Renvaktarstuga 1000000
Tjasskávárvásj 50
33
10
7
0
Skárjá
737
17
36
3
chaotic
Renvaktarstuga 0
0
3
10
87
Tjasskávárvásj 0
0
10
17
73
Skárjá
0
23
17
53
7

Measure
Mean
St.
dev.
4,7
4,23
3,97

0,47
0,68
1,07

1
1,8
1,87

0
0,85
1,11

5
4,27
3,07

0
0,91
1,08

1,17
1,37
2,57

0,46
0,67
0,94

As in the previous question, 100% of respondents described the Renvaktarstuga soundscape ascalm. Only 3% of people rated the last soundscape Skárjá as definitely restless. The average degree ofcompliance was the highest for the first soundscape, followed by the second and third soundscapes.
None of the soundscapes was considered chaotic by the respondents. 87% of respondents indicatedthe soundscape of Renvaktarstuga as the least chaotic. The mean value of the ratings was the highest
for the third soundscape, which of the three was considered to be the most consistent with theexamined statement.
All tested acoustic environments were described as pleasant. The soundscape of Renvaktarstuga
turned out to be the most calm and undifferentiated of all. The reason for this could be the small
number of sound sources present. The sounds of wind and grass were predominant in the soundscape.
The singing of the birds was virtually inaudible.
The soundscape of Skárja, which is much more varied and dynamic due to the presence of morevaried sounds, is different from the rest. In the acoustic environment, not only sounds of natural origin,but also those of human origin appear. More sound sources affect the dynamics and diversify the place.
Skárja's landscape was louder than the other two landscapes, which may have made it perceived asless pleasant. In Skarj's landscape, there are anthropogenic noises that disturb the natural harmony ofthe environment, making it perceived more negatively and not wanting to come back to it so often.
All studied landscapes are generally classified as pleasant, calm and not very varied.
For comparison purposes, calculations of basic eco-acoustic parameters were also carried out. The
calculation results are presented in Table 3.
The calculated values of the indicators confirm that the acoustic activity is not high. Slightly higher
values of ACI and Bio factors (Skarja, Tjasskávárvásj) indicate a slightly higher activity of birds.
Slightly higher values of the ADSI index (Skarja, Tjasskávárvásj) indicate the occurrence of higher
wind. Low ADI values (Renvaktarstuga) show that the soundscape is not containing many vocalizing
species. Higher values of the NDSI coefficient (Tjasskávárvásj) indicate greater biophonic activity
and minimal noise in 1-2kHz and indicate higher levels of biophonic activity in the soundscape.
Similar values of the AVE coefficient show that all three soundscapes are similarly saturated.

Table 3 - Basic eco-acoustic indicators calculated for 300 s recording window
Place

Renvaktarstuga

Indicator

Skárjá

Tjasskávárvásj

Index value

Acoustic Complexity Index

ACI

1823,25

1794.311785.92

Acoustic Diversity Index

ADI

0,002836

0.782465

0.850001

Acoustic Evenness Index

AVe

0.899937

0.820787

0.814957

Normalized Difference

NDSI

0.1074219

0.5146328

0.690243

Bioacoustic Index

Bio

1.526444.760373

3.389821

Amplitude Index

M

0.01587499

0.02330975

0.04241421

Total entropyH

0.3512573

0.6561064

0.7474927

Soundscape Index

3. SUMMARY
The conducted research allowed for the mapping of three unique soundscapes in the virtual reality
technology. Respondents generally classified the three soundscapes tested as pleasant and calm. The
presented simulations had a relaxing and calming effect on the respondents. The sounds of natural
origin had a positive effect on the psyche of the participants, and those of human origin usually
irritated and irritated them.
Strong relationships between the sound pressure level and the perception of a given environment
were also determined in the research. Additionally, it was noticed that the bad mood of the respondents
had a negative impact on the perception of the phonic image of the examined place. Natural acoustic
environments have also contributed to improving the mood of some of the respondents.
The relationships between the acoustic environment and humans were clearly discernible, eventhough all three studied soundscapes were natural and not polluted with noise, which only confirms
the colossal role of soundscapes in the life of the whole society.
VR technology allows for a very realistic representation of any acoustic environment. Modern
virtual reality devices can significantly facilitate the organization of soundscapes research, or evenenable it. In the future, research will be developed to compare different sub-arctic soundscapes to findthe relationship between them and to learn more about the impact of soundscapes on humans.

ACKNOWLEDGEMENTS
This work was funded by the Department of Mechanics and Vibroacoustics of AGH University of
Science and Technology in Cracow, Poland, grant number 16.16.130.942

REFERENCES
1. Brown A. L., Gjestland T., Dubois D.: Acoustic Environments and Soundscapes, CRC Press, 2015
2. Farina A.: Soundscape Ecology: Principles, Patterns, Methods and Applications. Dordrecht, Springer, 2014
3. Krause B.: The Great Animal Orchestra: Finding the Origins of Music in the World’s Wild Places. New
York, Little, Brown and Company, 2012
4. LaValle S. M.: Virtual Reality. Cambridge University Press, 2019
5. Schafer R. M.: The Soundscape: Our Sonic Environment and the Tuning of the World. Rochester, Destiny
Books, 1994
6. Wang M.: Social VR: A New Form of Social Communication in the Future or a Beautiful Illusion? Journalof Physics: Conference Series, IOP Publishing, 2020
7. ISO 12913-1:2014 Acoustics – Soundscape – Part 1: Definition and conceptual framework
8. ISO/TS 12913-2:2018 Acoustics – Soundscape – Part 2: Data collection and reporting requirements)
9. ISO/TS 12913-3:2019 Acoustics – Soundscape – Part 3: Data analysisis

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0315

Soundscapes on edge - The real-time machine learning approachfor measuring Soundscapes on resource-constrained devices
Nils KARGES1; Jeroen STAAB2,3; Jürgen RAUH1; Martin WEGMANN1; Hannes TAUBENBÖCK1,2
1
2

Institute of Geography and Geology, University of Würzburg, Würzburg, Germany

German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling, Germany
3

Geography Department, Humboldt-University Berlin, Berlin, Germany

ABSTRACT
According to the WHO, noise is a growing problem in urban areas and the second most common
environmental cause of health issues in Europe. As a complementary approach to noise maps based on soundpressure levels, soundscape maps can be a useful tool for urban planning, providing more information about
how people perceive acoustic environments. This study describes an in-situ soundscape monitoring system
based on WASN (Wireless Acoustic Sensor Network) for statistical spatial-temporal prediction ofsoundscapes in urban areas. Soundscape data on specifically defined spatial scales were observed andevaluated using a microcontroller with a 32-bit nRF52840 Nordic Semiconductors CPU and 1MB of memory
in a multifunctional urban area. The use of TinyML enabled machine learning algorithms provided state-ofthe-art soundscape classification to a low-cost edge device with extreme resource constraints regarding
memory, speed, and lack of GPU support. Sound source types are classified into anthrophony, traffic,
biophony, and geophony sounds using ESC-50 for evaluation. Our final MFCC-based CNN achieved an
accuracy of 81.6% and even reached higher accuracy in the enclosed studio test. The results show that it iscomputationally feasible to classify soundscapes on low-power microcontrollers and potentially inform
decision-makers based on extended sound analysis.
Keywords: Soundscape, TinyML, WASN

1. INTRODUCTION
The International Organization for Standardization (ISO) defines a soundscape as an "acoustic
environment as perceived, experienced, and/or understood by a person or persons in a given context"(1). Like a fingerprint of the moment, variable in space and time, urban soundscapes contribute to theperceived quality of the urban environment and the identity of a city. Each urban area has a unique
soundscape because of its multi-layered social and physical structure. There is a constant interplay
between society and space, in which space configurations are profoundly intrinsic to human activity(2). The presence of ambient sounds can evoke thoughts and emotions, influence our mood, and thus
guide our behavior (3–5). A listener trying to define a soundscape will always be influenced by thecomplex interactions of several variables, including physical, psychological, and physiological factors.
Even minor variations in a few physical characteristics can cause similar acoustic environments to beperceived differently (6). This complexity of these phenomena makes modeling soundscapes achallenging task (7). Because of the potential positive impact that an appropriate acoustic environment
can have on the well-being of citizens and the attractiveness of the city, the design of the acoustic
environment of public urban spaces has received considerable attention for two decades (8–10). It isthus one indicator to address urbanization problems to meet the United Nations' demands for"sustainable cities and communities" and "good health and well-being"(11).
1 nils.karges@uni-wuerzburg.de2

jeroen.staab@dlr.dejürgen.rauh@uni-wuerzburg.de4
martin.wegmann@uni-wuerzburg.de
5
Hannes.Taubenböck@dlr.de3

1

So far, noise mapping has mostly been based on sound pressure level (SPL) analysis. An example
is the large-scale noise mapping application using publicly available data, context-aware featureengineering, and the linear land-use regression (LUR) model by Staab et al. (15). Mapping noise
accurately and in its temporal variability remains a challenge. Beyond the standard noise maps
demanded for large cities and highways in Europe, there is a need to measure soundscapes in greater
detail. Therefore, we see our approach as a potential complement to maps based on sound pressurelevels. Measuring soundscape empirically at the edge can be a valuable tool for urban planning,providing more information about how people perceive acoustic environments. The soundscapeapproach adds additionally a significant number of acoustic parameters to characterize the dynamics
of the acoustic environment (see Appendix A in ISO 12913-2).
Traditional soundscape surveys are carried out by experts in-situ: the acoustic environments are
identified, and the time and location of their appearance logged. Intensive research on soundscapeindices in biodiversity and the emergence of soundscape ecology have given birth to new ideas onsoundscape taxonomy (16,17). Krause (27) used the terms "biophony" and "geophony": the first term
describes the complex arrangements of biological sounds and other ambient sounds and the latterdescribes the non - biological ambient sounds of wind, rain, lightning, etc. Later, Pijanovski et al. (16)extended the taxonomy with the "anthrophony" category, describing the sound created by humanactivities such as musical performance or oral conversation. In this work, the taxonomy of"anthrophony" is additionally extended with the sub-category of "traffic sounds" to provide
opportunities to test the influence of traffic in urban environments.
In previous studies, the dynamics of the acoustic environment became evident, which led to a new
impetus to continuously monitor the dynamic acoustic environments in cities (16,17). With the rapid
development of the Internet of Things (IoT) (18–20), sensors and embedded processors are becoming
smaller, cheaper, and with increasingly powerful computing capabilities. Solutions, which werepreviously difficult to achieve using small form factor and vast deployment, have now been madepossible through a combination of IoT and cloud technologies (21,22). There have already been
several attempts to use such smart devices to monitor and detect sounds on edge to reduce thebandwidth of data transmission to the central server and to maintain privacy (23,24). In order tocharacterize areas of a city and their sound environment, low-cost wireless acoustic sensor networks(WASN) are a cost-effective solution for centralized monitoring of the environment. Although various
WASN techniques have been developed to increase bandwidth efficiency, sensor nodes are typicallyused to collect data, neglecting their computational capacity and the potential benefits of acting as adata center to process the data. The Term edge processing is used, when data computation is done
directly on the smart sensor node or at the gateway to save battery power and ensure data privacy. It
allows organizations to analyze critical information at the node level and shorten the time to detect
anomalies (25,26).
In this paper, we propose a low-cost solution for a soundscape classifier with onboard audio
classification, embedded in a wireless sensor network based on a secure protocol. We focus on thelocal computing capability of the sensor nodes on classifying soundscape variables. Furthermore, we
test to what extent the sensors and their measured values are comparable in a studio environment.
Based on our results, we discuss the influencing factors that need to be answered in future research toclassify the soundscape variables more accurately.

2. SOUNDSCAPE SENSOR
In this paper we present a system architecture to classify urban environmental sounds consists ofthree layers: edge layer (sensing unit), fog layer, and cloud layer. As illustrated in Figure 1, the firstpart of the edge layer contains the sensor product described below, additio nally equipped with a longrange (LoRa) transceiver to collect, analyze and ultimately send the data via LoRa communication to
the IoT gateway. The Arduino Nano 33 BLE Sense, now acting as an edge device, brings the measured
data via machine learning into the inference, which describes the process of running data points intoa machine learning model to calculate an output. The second part describing the fog layer is operatingwith an ESP32 and a LoRa transceiver, representing the LoRa gateway connected to t he Internet. The
LoRa gateway is responsible for receiving the LoRa packets. The third layer of the IoT architectureis the cloud layer for cloud services and global storage as well as web application servers.

2

Figure 1 – The architecture of the soundscape monitoring system based on WASN.
2.1 Edge Device
There are infinite possibilities for different edge platforms (28), sensing units, and the softwarewith which the platforms can be programmed. In this paper, the powerful core, in combination withvery efficient energy consumption, high flexibility in terms of enabling/disabling modules, and thelow material cost, justified the decision to use the Arduino Nano 33 BLE Sense as the systems
development board and Figure 2 illustrating the individual modules attached to the development board.
It includes several environmental sensors and the ability to run machine learning models with TinyML
and TensorFlow™ Lite. Apart from the technical facts of different development boards, the support
and availability of tested libraries are also a relevant consideration in hardware selection. We prefer
to use development boards supporting a built-in digital signal processor module due to its significant
increases in performance. The development board is further equipped with a MEMS microphone(MP34DT05), with omnidirectional sensitivity (–26 dBFS ±3 dB sensitivity) and a 64 dB signal-tonoise ratio which has a low power consumption and is capable of capturing and analyzing sound inreal-time. Furthermore, the Arduino Nano 33 BLE Sense is equipped with the nRF52840 Nordic
Semiconductors processor, a 32-bit ARM® Cortex™-M4 CPU running at 64 MHz. It has 1MB ofprogram memory, and 256KB SRAM. The module size of the development board is 18x45mm (29).
The whole sensor module for environmental classification contains, besides the microcontroller, a
RTC DS3231 I2C real-time clock which is required to get the correct time of the recordings. A 3V cellcoin battery is added to retain the date and time while the power is off (e.g. when the main battery is
empty). In addition, the RTC can be used to wake up the board from sleep mode.

Figure 2 – The deployable sensor module for environmental classification. Scale for reference.
Furthermore, Figure 2 shows the 11 cm high, 5 cm wide, and 4 cm deep waterproof plastic housing
due to the TPE seal and a protection class of IP66/67 to secure the electronic components frommoisture. The additionally attached conical periphery is filled with a wadding wind protector todampen wind and noise. The development board also has a communications chipset equipped with a
NINA-B306 chip, installed for Bluetooth communication. The chip uses version 5.0 and supports BLE.
The NINA-B306 has an internal antenna and supports a maximum of 20 Bluetooth connections.
3

The BLE version of the soundscape sensor does not require external components except the battery
and the SD-card module. The LoRa version of the proposed environmental classifier consists of the
Nano 33 BLE Sense board, the NODEMCU ESP32 board, the LoRa RFM95 transceiver module in the
868 MHz band, a 6000 MAH lithium battery, and a 5 V voltage regulator. In this work, the NODEMCU
ESP32 acts as an interface between the Nano 33 BLE Sense and the LoRa RFM95 transceiver module.
ESP32 and Nano 33 BLE Sense are connected using UART serial communication protocol.
2.2 IoT architecture
The entire IoT architecture can be divided into three parts: The first part is the initialization of thesystem communication protocol, which is started by the soundscape sensor node. The second partcontains the data handler (IoT gateway). This part is responsible for receiving the data packets sentfrom the edge device (soundscape sensor) and converting them from byte to float format. In this format,
they can be sent to Node-Red in the last part of the IoT gateway application for analysis andvisualization.
The developed system consists of a network of sensors forming a typical WASN, capable ofretrieving data from the sensors and sending it to a cloud server. Another feature is the ability tocommunicate via LoRa peer-to-peer network using the RFM95 LoRa transceiver (30). This enables
long-range spectrum communication, providing high-frequency immunity while maintaining low
power consumption (31). The sensor node (soundscape sensor) describes the lowest level of our
WASN, whose purpose is to collect data from the connected sensors and send the classified results to
the IoT gateway. To accomplish these tasks, the sensor node requires a microcontroller and a set of
sensors that can be permuted from node to node as needed for a specific solution. The sensor nodesconsist of an ESP32, an RFM95 module, and the soundscape sensor for retrieving information whichis then sent to the aggregation node.
The IoT gateway describes the solution to enable IoT communication. Here, the receiver unit
consisting of ESP32 and RFM95 receives the data packets from the sensing node of the soundscapesensor and the RMF95. The receiver unit then sends the data to the cloud for global storage orvisualization. The RFM95 LoRA transceiver module communicates with the ESP32 using the serial
peripheral interface (SPI) communication protocol (32). Furthermore, the sensor data is sent via the
IoT gateway to the IoT Cloud, where it is processed in real-time and visualized with Grafana. Grafana
is a web-based visualization dashboard where the information must first be converted into the NodeJS format using NodeRed. From NodeRed, the data is transferred via the API to the InfluxDB database,from where it can be queried into Grafana for further visualization. Although the technical setup isvery complex, we endorse easy accessibility to the results for end users eventually.

3. METHODS
The proposed architecture attempts to solve the research problem of obtaining privacy in soundmeasurements in urban areas by moving the machine-learning processing from the cloud to the edge
(24,33). The edge computing approach is being introduced to address the disadvantages based on
cloud technology. As illustrated in Figure 3, the inference is executed directly on the microcontroller
at the edge, allowing the system to operate in areas with poor or unstable Internet while protecting
individuals' privacy. The sounds recorded by the microphone, which can include conversations
between individuals, are processed on the microcontroller. The classified values alone are forwarded
to the cloud layer to be processed by the end-user. Additionally, it has the advantage of reducing
bandwidth, which enables the use of long-range but limited-bandwidth communication technologies
such as LoRa (31).
Edge processing
The environmental classifier starts the inference by recording sound snippets with the microphone,which describes the first step in Figure 3. This recorded sound is passed to the feature extractor, which
processes the signal as a Mel Frequency Cepstral Coefficient (MFCC). This part of the application is
responsible for managing the sounds captured by the microphone and describes the input tens or of themodel. The application of the acquisition unit describes the TensorFlow lite interpreter, which is
directly connected to a TensorFlow Lite model. This part of the application is responsible for inference
based on the environmental sound recordings captured by the microphone. Subsequently, theConvolutional Neural Network (CNN) model is integrated into the system as a C byte array, describing
3.1

4the activity predictor, which determines the model’s output and decides on a prediction based on
thresholds. In the last step, the environmental classifier sends the data from the sensing unit to thesystem gateway via LoRa. After the device is set up, the loop function is called endlessly and is set tosleep mode when the classification results are successfully transmitted. If the transmission fails, themessage will be resent up to five times and then stored on the SD card. The message is limited to 85
Bytes and consists of the header with an identifier, a timestamp, and the classification results(anthrophony, traffic, biophony, geophony).

Figure 3 – Audio transmission process from detecting the signal to the voting for one of the fourclassification classes.
3.2 Dataset
To describe the machine learning process on edge, it is crucial to explain the dataset from which
the model learns. Here, many datasets exist to identify environmental sounds that sample large
numbers of audio with labels for each sample identifying which sound it is (34,35). Due to its versatiletraining data, the dataset used in this project is the ESC -50 from Piczak (36). The ESC-50 dataset
includes a comprehensive summary of results on its Github repository with over 40 ent ries. The bestmodels to date (July 2022) achieved 97.00% accuracy (37). The ESC-50 dataset from Piczak, suitablefor comparing environmental sound classification methods, is a labeled collection of 2000
environmental audio recordings. The recordings in the dataset consist of 5 -second recordings divided
into 50 semantic classes, loosely arranged into five main categories. To organize the five major
categories of the ESC-50 dataset into the taxonomies adapted from Krause and Pijanovski, the class"animals" is renamed biophony, and the class "natural soundscapes & water sounds" is renamed
geophony. The categories "Human, non-speech sounds" and "Interior/domestic sounds" have been
reclassified to the anthrophony class, representing human-generated sounds. The class ofexterior/urban noises was empirically divided into the class of traffic sounds and anthrophony. On the
one hand, traffic sounds can be understood as a subcategory of anthrophony sounds. On the other hand,traffic noise plays a unique and significant role in the urban context, so these sounds generated by
vehicles (and thus by techniques) should be differentiated from such sounds that originate directlyfrom humans. That is why we have designated traffic sounds as a separate category. The dataset now
5consists of anthrophony sounds (As), geophony sounds (Gs), traffic sounds (Ts) and biophony sounds(Bs) as illustrated in Table 1.
Table 1 – After Piczak (2015) 5-second-long recordings organized into 28 semantical classes

biophony Sounds
Dog
Insects (flying)
Crow
Sheep
Chirping birds
RoosterPigarranged into 4 major categories.
anthrophonygeophony Sounds
SoundsRain
Footstep
Sea waves
Laughing
Wind
Door knock
Pouring water
BreathingThunderstorm
People talking
Crackling fire
Clapping
Water drops
Coughing

traffic Sounds
Car sound
Helicopter
Siren
Car horn
Train
AirplaneEngine

3.3 Preprocessing
In the preprocessing, the signal in the form of a MFCC gets passed with a frame length of 0.02
seconds to the feature extractor. As shown in Table 2, the frame length, frame stride, FFT length, thenumber of coefficients, the number of filters, and the low to high-frequency band edge of the melscale filterbanks, as well as a local mean normalization of the signal, are extracted from the MFCC.
Furthermore, different data augmentations like adding white noise or masking time - and frequency
bands are performed to reduce the risk of overfitting due to data inadequacy and improve the efficiency
and accuracy of the model.
Table 2 – Description of the individual parameters of the model.
Parameters
ValuesSamplerate (Hz)19000
Number of coefficients
13
FFT length (samples)
256
Frame lenght
0.02
Frame stride
0.01
Filter number
32
EpocAs100
Learning rate
0.005
Training samples
2000
Validation samples
400
3.5 Training and performance
The training was performed for each individual analysis window, with each window having theassociated label added to the audio clip (anthrophony, biophony, geophony, traffic). 75% of the soundsof the dataset was used as a training set, and 25% of the dataset were kept as a validation set. During
training, audio clips are randomly selected from the four categories in the training set. For each audio
clip, a time window is selected at a random position. In this way, the time -shifted data expansion iseffectively implemented. To evaluate the model for the entire audio clip, an additional passover thevalidation set combines the predictions from multiple time windows. The learning rate used in thismodel was set to 0.005 and trained for up to 100 epochs. The training cycles were limited to 100
cycles for this test, as there is a risk that the data set is too tuned for the spe cific test set and might
perform poorly with new data (overfitting). A summary of the experiment settings can be found in
Table 2.
3.6 Evaluation
To evaluate the predictions of the resource-constrained device, the classifier is tested on two levels:
First, the baseline is tested, where the training data is tested against the validation data . Second, live
audio data is recorded from the built-in microphone and then classified. Subsequently, the selected
6

models are evaluated on the test set in each fold, giving us the baseline results used to compare against
the results that our microcontroller will return once we run our model ther e. Further to the standard
cross-validation for ESC-50, the model performance is also evaluated by separating foreground andbackground noise. Therefore, a confusion matrix is created in RStudio using the caret (38) andgmodels (39) package. The first confusion matrix is generated describing the baseline, calculatedusing the training data from the ESC-50. The second confusion matrix is derived from the result fromthe live audio-recording by the microcontroller.
Next, the trained models were tested on the development board with live audio on the microphone.
To check the output of two sensors for similarity, both sensors are placed next to each other in frontof two active near-field monitors (Adam A5X). Traffic sounds, anthrophony sounds, biophony sounds,
and finally geophony sounds are played on the speakers in 5-minute sequences over a period of 1 hour.
All soundscape sequences represent 1-hour recordings of youtube.com scenes according to thecriterion of presenting the purest possible sound from the respective class. For example, a 1 -hoursoundscape with only traffic noise was used for the traffic sounds class, a sound file with rain andstorm sounds was used for geophony sounds, and an hour full of bird sounds was used for biophonysounds. In contrast, the anthrophony class was represented with a long walla (reflecting the varying
textures of crowd noise in the different countries) of people on the market side. In one hour, all fourclasses were repeatedly classified three times each. Here, the sensor is subjected to a test that simulates
the measurement in the natural environment and plays soundscape recordings from the fourclassification classes for the sensor to evaluate the recordings. The experiment is shown in Figure 6
in the form of a plot divided into a dot plot in the lower part and a stripe plot in the upper part. Thus,
the dots indicate the classification share of each recorded second, and the stripes describe the majority
vote of each classification share per second. The resulting dataset serves as the basis for calculating
the confusion matrix from Figure 5b).

4. RESULTS
4.1 Training performance
The loss diagram and the accuracy diagram in Figure 4 show that at the beginning of the first epochs,
the model indicates comparable performance in both the training and the validation data sets.
Subsequently, it can be seen that the parallel plots start to diverge more and more with more epochs.
The accuracy of the training and validation data shows a similar tendency. The val_accuracy,
indicating the accuracy of the predictions of a randomly selected validation data set after each training
period, does not decrease but remains constant at about 80%, whereas the overall accuracy continued
to increase. The overall loss decreases after almost every epoch and approaches 0. Like theval_accuracy, the val_loss, describing the value of the cost function for the cross-validation data,
appears to be stagnant.

Figure 4 – Performance of the loss function of the training and validation datasets on the left and theaccuracy of the train- and validation data on the right.

7

4.2 Evaluation
The confusion matrix of the baseline model shows the most accurately predicted classes wereanthrophony sounds (As) (91.3%), traffic (Ts) (89.1%), followed by the average performance of theclasses biophony sounds (Bs) (71.4%) and geophony sounds (Gs) (74.7%). In addition, 22.6% of thesounds labeled biophony and 13.6% of the geophony sounds were misclassified as anthrophonysounds. Furthermore, 9.9% of biophony sounds were misclassified as traffic sound and 9.4% of thetraffic sounds were classified as anthrophony sounds. As described in Figure 5, we use the results ofthe baseline model (a)) to compare with the results of the sounds recorded in the test environment (b)).

Figure 5 – a) The confusion matrix of the baseline model. b) The confusion matrix of the sounds recordedin the test environment. Displayed in percent (%).
As can be seen in Figure 5b), the class of the anthrophony sounds was best classified with 83.78%,
closely followed by class traffic sounds with 83.11%. The class of the biophony sounds has aclassification accuracy of 67.33%, representing a decrease in classification accuracy of 4.07%
compared to the baseline confusion matrix. geophony sounds was classified with 71.79%.
anthrophony sounds show a lower classification accuracy of 7.52% and a 5.99% decrease in theaccuracy while classifying traffic sounds. The overall accuracy achieved by the baseline model is
81.6% and the model accuracy of the measurement in the test environment, achieved 76.50%. The
similarity of the classification results is visible but still shows slight variations in the individual
sequences. Some of the classification results show clear patterns in Figure 6, it can be seen that theclasses are repeatedly confused with similar classes. In the stripes of the traffic sounds, it can be seen
that, as shown in Figure 5b.), 22.98% of geophony sounds and 10.44% of biophony sounds are
misclassified. The class of biophony sounds offers a relatively homogeneous classification accuracy
and is only sometimes confused by misclassifications of anthrophony sounds with 8.67%. The classof geophony sounds is most affected by misclassifications. Here the most confusions occur in the areaof biophony sounds with 20.89% and 7.44% for traffic sounds. Additionally, can be seen that mostmisclassifications appear in the lower classification share.

Figure 6 – The sensor's classification share per second.
8

4.3 Output
Once the ML-based soundscape classifier has been evaluated, the soundscape sensor can be
deployed and described in terms of the data it provides. As shown in Table 3, the date, time, andgeographical position (lon,lat) and the predictions from one of the four classes are placed in a separate
column. The prediction value shown is referred to as the classification share. This data format is thenreceived as a .txt file from the IoT gateway, and the metadata is transmitted via LoRaWAN to thecloud and visualized via Grafana. This empowers urban planners and gives insights into spatialtemporal soundscape characteristics of different urban areas.
Table 3 – Representation of the output format of the environmental classifier with the division of the
Date

Time

01.01.2022
01.01.2022
01.01.2022…
01.01.2022

05:00:00
05:00:10
05:00:20…
21:59:59

predictions into the individual classes.
LonLatgeophonybiophony
share
share
9.565957 49.474098
0.09
0.14
9.565957 49.474098
0.15
0.06
9.565957 49.474098
0.12
0.04
…
…
…
…
9.565957 49.474098
0.82
0.14

anthrophony
share
0.54
0.00
0.28
…
0.04

traffic
share
0.22
0.79
0.56
…
0.00

The Heatmap visualization in the monitoring dashboard displayed in Figure 7, shows an example ofthe change in soundscape classifications spread over a whole day (05:00- 21:59) at a random test site.
The location describes a site of high entropy in terms of its land use and includes a park and a roadintersection with a pedestrian crossing within a 250m radius. Here, the emerging classification shareat the given time plotted on the x-axis is shown for each class. The heatmap graphic chosen in thispaper is just one of many visualization options of this web service, and only there to show that thewhole system can monitor Spatio-temporal features of the soundscape at multiple locations.

Figure 7 – Visualization of the data flowing into the cloud layer.

5. DISCUSSION
The methodology used and the goal we set, namely to classify the basic sources of a soundscape,work in principle. Still, there are some things to consider when evaluating the on-sensor classification
process. On the one hand describing the model performance with the interaction between feature
representation and RAM and CPU usage as well as the classification set-up itself. The reduced feature
representation with a reduced number of predictions is used to minimize the peak RAM usage andflash usage so that the model can perform on the microcontroller. Compared to the SB-CNN bySalamon and Bello (40), the analysis window used is lower (1000 ms versus 1765 ms) and leads to a
reduction of RAM and CPU usage. Furthermore, we keep the window increase low (250 ms), which
decreases the performance, but also requires much less CPU usage.
In order to address potential measurement issues, the microphone itself has challenges: its
placement and orientation, its spatial context as well as the temporal context of the measurements.
Regarding the microphone, the acquisition circuit forms the subsystem for acoustic data acquisition.
Here, the selection criteria for microphones are usually frequency range, flat frequency resp onse andhigh sensitivity. The MP34DT05 is a Class 2 microphone where Class 1 microphones have a wider
frequency range with less error tolerance then those of class 2. Furthermore, it is expected that thesignal to noise ratio on the onboard microphone is lower compared to an external microphone.
However, it is important to note that a trade-off between performance and cost is taken into account,9as one of the main requirements is to keep the cost low in order to be able to create large scale sensor
networks (41).
A possible reason for the relatively low accuracy is the class imbalance of the ESC -50 dataset.
Here, the anthrophony sounds as well as the traffic sounds predominate, and this is reflected in the
confusion matrix of the model itself (Figure 5). We consider it crucial to design a training data set toanalyze soundscapes to counteract underrepresented classes. The overall accuracy of the base model
reached 81.6%, and the model accuracy of the measurement in the test environment reached 76.50%.
The system proposed in this paper and the high classification results can support the soundscapeanalysis. Furthermore, the impact of the lower accuracies on application studies needs to be evaluatedin future studies. The loss function in Figure 4 clearly shows convergence but is not overfitting. The
decaying of the learning rate, shown in the form of the plateau of the validation set, could be due tothe relatively high value of 0.05. When comparing the baseline model accuracy with the accuracy ofthe practical test application, a decrease in the performance given by the studio conditions aremeasured. We assume that this could be because there are fewer different sounds in the natural
application area than the large number of sounds represented in the dataset. Additionally, the mostrepresentative sound examples used in the test dataset give an even more precise representat ion of theenvironmental sounds. It also needs to be mentioned, that the recordings of the respective classes
sometimes also contain a class overlap. This is due to the fact that, on roads with predominantly traffic
sounds, geophony sounds are usually also represented. Furthermore, as seen in the model, someclasses like geophony and traffic sounds are often confused with each other because they serve similarfrequencies, as seen in the spectral similarity. Based on these observations, there is likely a subtle
similarity in their Log-Mel spectrogram features that the model cannot discriminate. The mixture of
the individual classes in their respective weighting and background and foreground noises also
appeared helpful in a soundscape characterization and are suggested to be analyzed in future studies.

6. CONCLUSIONS
Efficient methods for assessing and monitoring environmental sound diversity are central to urban
research and critical to sound management. Technological advances in acoustic remote sensing are
inspiring new approaches using soundscapes to understand and identify major ecological or humanoriented sound patterns by understanding the interrelationships of different sound sources such asanimals, people, or the environment itself. Intensive descriptive methods capturing soundscapes are
required for urban planning or design, with the ultimate goal of creating spaces of high acoustic quality.
Our proposed end-to-end IoT system combines machine listening at the edge with cloud services
that provide real-time analytics of urban residential areas. At the edge, each sensor node performs
sound classification. Due to the small size, low price, and low power consumption of LoRaWAN
transmission in WASN, the presented soundscape sensors make it possible to extend its coverage andincrease the Spatio-temporal density with limited rersources.
Further development potential is, e.g., to vary the granularity of the classification types at different
locations to capture the likely predominant sound events at a given area more accurate. Various
enhancements are currently being made to this system to improve its capabilities further. One of theseenhancements is including a multi-label sound event localization and detection (SELD) system (42,43),
as reported in the DCASE 2020 competition. Our proposed approach also provides customizable datavisualization to monitor each sensor node in real-time. It includes retrospective descriptive analysis
using aggregated metadata of the sound received by our sensor network. Although the quality of thedata collected remains to be investigated, networks of these monitors placed at app ropriate locations
allow addressing a spatially dense soundscape monitoring across a city, ensuring a more reliable urban
soundscape management with lighter installation and maintenance costs.

ACKNOWLEDGEMENTS
This study was partly funded by the German Federal Environmental Foundation (DBU).
Furthermore, this research would not have been possible without the countless tools from the EAGLE
Master program (University of Wuerzburg) used to construct the sensor.

10

REFERENCES
1. 14:00-17:00. ISO 12913-1:2014 [Internet]. ISO. [cited 2022 Jun 9]. Available from:https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/05/21/52161.html
2. Lefebvre H, Nicholson-Smith D. The production of space. Vol. 142. Oxford Blackwell; 1991.
3. Axelsson Ö, Nilsson ME, Berglund B. A principal components model of soundscape perception. The
Journal of the Acoustical Society of America. 2010 Nov;128(5):2836–46.
4. Hong JY, Jeon JY. Influence of urban contexts on soundscape perceptions: A structural equation modelingapproach. Landscape and Urban Planning. 2015 Sep 1;141:78–87.
5. Non-auditory factors affecting urban soundscape evaluation: The Journal of the Acoustical Society ofAmerica: Vol 130, No 6 [Internet]. [cited 2022 Jun 10]. Available from:https://asa.scitation.org/doi/abs/10.1121/1.3652902
6. Kang J, Aletta F, Gjestland TT, Brown LA, Botteldooren D, Schulte-Fortkamp B, et al. Ten questions on
the soundscapes of the built environment. Building and Environment. 2016 Nov 1;108:284–94.
7. Aletta F, Kang J, Axelsson Ö. Soundscape descriptors and a conceptual framework for developing
predictive soundscape models. Landscape and Urban Planning. 2016 May;149:65–74.
8. Zhang M, Kang J. Towards the Evaluation, Description, and Creation of Soundscapes in Urban OpenSpaces. Environ Plann B Plann Des. 2007 Feb 1;34(1):68–86.
9. Yu L, Kang J. Factors influencing the sound preference in urban open spaces. Applied Acoustics.
2010;71(7):622–33.
10. Herranz-Pascual K, Aspuru I, Iraurgi I, Santander Á, Eguiguren JL, García I. Going beyond quietness:
Determining the emotionally restorative effect of acoustic environments in urban open public spaces.
International journal of environmental research and public health. 2019;16(7):1284.
11. Nations U. Sustainable Development Goals [Internet]. United Nations. United Nations; [cited 2022 Jun10]. Available from: https://www.un.org/en/sustainable-development-goals
12. Peckens C, Porter C, Rink T. Wireless Sensor Networks for Long-Term Monitoring of Urban Noise.
Sensors. 2018 Sep;18(9):3161.
13. Zannin PHT, Ferreira AMC, Szeremetta B. Evaluation of Noise Pollution in Urban Parks. Environ Monit
Assess. 2006 Jul 1;118(1):423–33.
14. Chouard CH. [Urban noise pollution]. C R Acad Sci III. 2001 Jul 1;324(7):657–61.
15. Staab J, Schady A, Weigand M, Lakes T, Taubenböck H. Predicting traffic noise using land-use
regression—a scalable approach. Journal of exposure science & environmental epidemiology.
2022;32(2):232–43.
16. Pijanowski BC, Villanueva-Rivera LJ, Dumyahn SL, Farina A, Krause BL, Napoletano BM, et al.
Soundscape Ecology: The Science of Sound in the Landscape. BioScience. 2011 Mar 1;61(3):203–16.
17. Pijanowski BC, Farina A, Gage SH, Dumyahn SL, Krause BL. What is soundscape ecology? An
introduction and overview of an emerging new science. Landscape Ecol. 2011 Nov 1;26(9):1213–32.
18. Mois G, Folea S, Sanislav T. Analysis of three IoT-based wireless sensors for environmental monitoring.
IEEE Transactions on Instrumentation and Measurement. 2017;66(8):2056–64.
19. Lee HC, Ke KH. Monitoring of large-area IoT sensors using a LoRa wireless mesh network system:
Design and evaluation. IEEE Transactions on Instrumentation and Measurement. 2018;67(9):2177–87.
20. Gómez JE, Marcillo FR, Triana FL, Gallo VT, Oviedo BW, Hernández VL. IoT for environmental
variables in urban areas. Procedia computer science. 2017;109:67–74.
21. SONYC: a system for monitoring, analyzing, and mitigating urban noise pollution: Communications of
the ACM: Vol 62, No 2 [Internet]. [cited 2022 Jun 13]. Available from:https://dl.acm.org/doi/abs/10.1145/3224204
22. Wong T, Watcharasupat KN, Lam B, Ooi K, Ong ZT, Karnapi FA, et al. Deployment of an IoT System
for Adaptive In-Situ Soundscape Augmentation [Internet]. arXiv; 2022 [cited 2022 Jun 13]. Available
from: http://arxiv.org/abs/2204.13890
23. Hassan AM, Awad AI. Urban transition in the era of the internet of things: Social implications and privacy
challenges. IEEE Access. 2018;6:36428–40.
24. Atlam HF, Wills GB. IoT security, privacy, safety and ethics. In: Digital twin technologies and smart cities.
Springer; 2020. p. 123–49.
25. Segura-Garcia J, Felici-Castell S, Perez-Solano JJ, Cobos M, Navarro JM. Low-Cost Alternatives for
Urban Noise Nuisance Monitoring Using Wireless Sensor Networks. IEEE Sensors Journal. 2015
Feb;15(2):836–44.
26. Picaut J, Can A, Fortin N, Ardouin J, Lagrange M. Low-Cost Sensors for Urban Noise Monitoring
Networks—A Literature Review. Sensors. 2020 Apr 16;20(8):2256.
1127. Krause B. Anatomy of the Soundscape: Evolving Perspectives. JAES. 2008 Jan 15;56(1/2):73–80.
28. Chen J, Ran X. Deep learning with edge computing: A review. Proceedings of the IEEE.
2019;107(8):1655–74.
29. Nano 33 BLE Sense | Arduino Documentation [Internet]. [cited 2022 Jun 13]. Available from:https://docs.arduino.cc/hardware/nano-33-ble-sense
30. Firdaus R, Murti MA, Alinursafa I. Air quality monitoring system based internet of things (IoT) using
LPWAN LoRa. In: 2019 IEEE international conference on internet of things and intelligence system(IoTaIS). IEEE; 2019. p. 195–200.
31. Bor M, Vidler JE, Roedig U. LoRa for the Internet of Things. In AUT: Junction Publishing; 2016 [cited
2022 Jun 28]. p. 361–6. Available from: https://eprints.lancs.ac.uk/id/eprint/77615/
32. Leens F. An introduction to I 2 C and SPI protocols. IEEE Instrumentation & Measurement Magazine.
2009;12(1):8–13.
33. Lv Z. Security of Internet of Things edge devices. Software: Practice and Experience. 2021;51(12):2446–
56.
34. Salamon J, Jacoby C, Bello JP. A dataset and taxonomy for urban sound research. In: Proceedings of the
22nd ACM international conference on Multimedia. 2014. p. 1041–4.
35. Cartwright M, Mendez AEM, Cramer J, Lostanlen V, Dove G, Wu HH, et al. SONYC urban sound tagging(SONYC-UST): A multilabel dataset from an urban acoustic sensor network. 2019;36. Piczak KJ. ESC: Dataset for Environmental Sound Classification. In: Proceedings of the 23rd ACM
international conference on Multimedia [Internet]. New York, NY, USA: Association for Computing
Machinery; 2015 [cited 2022 Jun 13]. p. 1015–8. (MM ’15). Available from:https://doi.org/10.1145/2733373.2806390
37. Piczak KJ. karolpiczak/ESC-50 [Internet]. 2022 [cited 2022 Jul 4]. Available from:https://github.com/karolpiczak/ESC-50
38. Kuhn M. caret: Classification and Regression Training. Astrophysics Source Code Library. 2015 May
1;ascl:1505.003.
39. Warnes GR, Bolker B, Lumley T, Warnes MGR, Imports M. Package ‘gmodels’. Vienna: R Foundationfor Statistical Computing. 2018;
40. Salamon J, Bello JP. Deep Convolutional Neural Networks and Data Augmentation for Environmental
Sound Classification. IEEE Signal Processing Letters. 2017 Mar;24(3):279–83.
41. Liu B, Towsley D. A study of the coverage of large-scale sensor networks. In: 2004 IEEE International
Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat No04EX975). 2004. p. 475–83.
42. Nguyen TNT, Jones DL, Gan WS. Ensemble of Sequence Matching Networks for Dynamic Sound Event
Localization, Detection, and Tracking. In: DCASE. 2020. p. 120–4.
43. Tan EL, Karnapi FA, Ng LJ, Ooi K, Gan WS. Extracting Urban Sound Information for Residential Areas
in Smart Cities Using an End-to-End IoT System. IEEE Internet Things J. 2021 Sep 15;8(18):14308–21.

12

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0489

Audio-visual tools as support for urban sound planning policies
Luigi MAFFEI , Massimiliano MASULLO , Aniello PASCALE , Samiha BOUCHERIT , Hanife Ayca
1

DOGAN ISERI

1

1

1

2

1

Department of Architecture and Industrial Design, University of Campania “Luigi Vanvitelli”

2

Department of Architecture, Eskisehir Technical University, Eskisehir, Turkey

ABSTRACTFor urban sound planning policies, several tools are considered as support to the decision. Noise maps
elaborated with traffic and fixed sources emission and propagation models represent the basic level of
information. They permit to evaluate areas and the number of people that are overexposed during day andnight and consequently they can help in the formulation of action plans to reduce the noise impact. A networkof sensors distributed inside the urban context can be a second level of information. Receiving more real dataon the variability of the sound can help in elaborating noise control activities in certain areas. However, thesetools lack the judgment and feedback received from the people. This information is fundamental for a human
centered urban sound planning strategy.
In this paper it is presented the potentiality of the combined use of 360-degree video, that recently has become
popular for the representation of the space, and 3D ambisonic audio-recording which permits high quality
reproduction. With this combination it is possible to build-up urban audio and visual maps that canconfigurate better the reality and that can involve more efficiently policy makers and inhabitants in the wholeprocess of the evaluation and planning
Keywords: Audio-visual, urban sound, planning policies.

1. INTRODUCTION
Urban environmental planning is a continuous dynamic process. As a result of an intensive
urbanization it is expected that the population of cities will reach 6.5 billion by year 2050 (1). This
increase in population will cause a hindrance to the urban planners to ensure the sustainability of cities
and to meet the needs of current and future generations. Searching innovative solutions to create agood and sustainable urban environment (2) is then an imperative target.
In environmental noise management, the study of the urban sound environment is mainly based on
assessing noise as the Sound Pressure Level (SPL) at the source, or A-weighted sound equivalentlevels at receivers representing the first step toward the noise mapping, zoning and monitoring oflarge anthropized areas. However, despite their positive effect on noise planning and predicting future
mitigation strategies, they have various limitations due to the simplified modelling of the source,
providing noise maps only valid for the time and characteristics (e.g. traffic flows, percentage of lightand heavy vehicles) of investigation (3).
Further, urban and architectural currents began calling for adopting new approaches. Recent studies
indicate that this vision plays a dominant role in perceived experiences (4) , and the high-quality
acoustic environments can function as 'resources' that may positively affect recovery fr om
physiological stress (5) and can be regarded as essential components of restorative environments (6 7).
Concurrent with this development, new technologies have emerged that can be more than effectivein evaluating urban space as the 360-degree video and 3D Ambisonic audio-recording. Recently, 360°
cameras have become popular, which provides new challenges and opportunities for creating
omnidirectional mixed reality (8). 360° videos are rapidly growing as more and more major content
providers and news outlets adopt the format to enrich the user experience (9).

Hence, this paper aims to provide an intervention reviewing the possibility of combining 360 degree video with 3D ambisonic audio-recording. This combination could allow the creation of visualaudio urban maps, which optimally represent reality and help urban designers and decision -makers tointegrate the citizen’s assessment into the design process within the framework of impact andeffectiveness.

2. Common approaches to urban sound planning:
To address the long-term impact on the environment and communities due to urban development
projects, most countries have adopted planning policies and regulations that address sustainability andenvironmental concerns. In particular, dealing with traffic noise a dversely affects people's health andwell-being, and the application of EU Environmental Noise Assessment and Management Directive(END) has led to several studies (10) focusing on noise reduction through various methods . The mostimportant are presented below.
2.1. Noise maps
According to EU directive (10) the Noise map; “shall mean the presentation of data on an existingor predicted noise situation in terms of a noise indicator, indicating breaches of any relevant limit
value in force, the number of people affected in a certain area, or the number of dwellings exposed tocertain values of a noise indicator in a certain area”. Noise maps directly show the current life noiselevel and are one of the most important tools to evaluate the acoustic environment.
A noise map is a graphical representation of the spatial distribution of the magnitude of noise leveldescriptors (e.g. L DEN , L night ) in a given region for a given period of time. Noise maps become areference for policy makers regarding environmental noise control (11). Noise maps are generated
based on the existing or predicted traffic flow typologies, volumes (number of vehicles per hour) andcomposition (percentage of light and heavy vehicles), as well as on several additional characterist ics
of the road surface (e.g. slope, absorbent pavement) and of the surrounding environment (e.g.
walls/barriers, buildings). Nevertheless, it is well known that the noise maps are not related to theacoustic quality of sound and consequently cannot be used to describe the complex relationshipbetween sonic environment and humans. Moreover, noise maps require constant updating as they
represent noise levels during inspection. Urban acoustic environments are primarily inspected fornoise (10).
Environmental noise control research supports more holistic approaches than noise reduction in
urban acoustics (12-14). Local authorities, on the other hand, focus more on qualitative practices intheir strategic planning for noise-related problems. This is generally accepted as an effective andpractical way to achieve better urban acoustic environments (15-17). However, methodology researchis still ongoing to combine quantitative and qualitative approaches for urban planning. Various
attempts are being made to apply typical methods of noise mapping to soundscapes (18).
2.2. Networks of Sensors
Networks of Sensors are an alternative for in situ measurements. In situ measurements are required
to make noise maps more realistic. However, considering the spatial changes of the investigated urban
scale and environments, the number of measurement points must be very large (19). Networks of
Sensors offer flexible measuring points. At these points, measurements are made in short periods.
Expanding networks of sensors of measurement points is very costly, and aiming to produce noisemaps in this way is unrealistic (20). Recently, technical devices with optimized size and power
consumption and integrating various wired or wireless communication systems are emerging. With
the developing technology, these devices are becoming more affordable. IoT-based environmental
monitoring tools have started to be used in many areas, such as smart cities.
It should be noted that the placement and application of networks of sensors created for aco ustic
measurements can be complex. For this reason, networks of sensors that can be created can be asolution to the limited scope of noise observation points to obtain rich acoustic information in areas
where noise analysis is desired. There may be deficiencies in the acoustic performance of networks
of sensors. When the networks of sensors applied with various hardware are installed during theapplication, they may experience a decrease in performance by being exposed to environmental effects.

This is likely to adversely affect the quality of instant reviews.
2.3. Binaural recordings
Binaural recordings are a broad form of stereo recording. Ideally, binaural measurements are only
taken at the left and right ear positions. Therefore, binaural recordings, when played back by equalized
and calibrated headphones, can reproduce the sound closest to what can be heard with the human ear.
By recording sound at the level of the eardrum, all necessary clues are taken to determine the locationof the sound in the environment (21).
In the acoustic measurements to be made about the soundscape; the way people perceive t heacoustic environment should be taken into account. For this reason, calibrated binaural measurement
systems should be used to record the acoustic conditions of an environment. Conditions that are as
close as possible to people's hearing system and sensations should be chosen in the recordings. Since
microphone and recording technologies are constantly changing, some conditions brought bytechnology are beneficial, but these technological devices are far from standardization and make it
difficult to make accurate analyzes in the calculation of psychoacoustic parameters (22).

3. Holistic approach for urban sound planning applied
When looking at common approaches in urban sound planning; the soundscape approach offers ahuman-centered perspective that evaluates the perception of the acoustic environment independentlyof positive or negative judgments. For this reason, the soundscape approach is a step forward in noise
control since it doesn't detect just noise (23-25). It focuses on human experiences and responses tothe acoustic environments with a human-centered perspective (26).
Moreover, the perception of space is a multifaceted process in which persons direct their behavior
in the environment under sensory stimuli influence (27). From this, urban designers realize that urban
environmental planning should be developed for people and with people, engaging citizens toparticipate in all stages of the planning process actively. Along with health effects and other factors,
urban designers have also contributed to different scientific studies on the need to include humanperception in the design process (28). Human beings are an important element of a designed
environment (29) and natural acoustic environments are beneficial to human mental health (30). Visualand acoustic information can reinforce or exhibit interference (31). Hence, it is logical to explore
subjective responses to both sounds and vision in urban spaces to develop comprehensive and practicalguidelines for health-promoting environmental design (32).

4. Innovative approaches to urban sound planning
4.1. Visual stimuli of 360-degree videos
Recent technological advancements, such as the Google Street View program(www.google.com/maps), may provide an alternative mechanism to traditional personal roadsideinspections (33). It provides another data source for the neighborhood environment. Street View,
available through Google's online maps application (maps.google.com), is a library of images fromcars driving down the street. The images are processed to provide a street -level panoramic view ofcity streets where users can navigate forward or backwards along the street, pan 360 degrees, rotate
the camera vertically 290 degrees, and zoom in and out, allowing users to walk from their computer
almost anywhere, on any available street. Therefore, this desktop-based tool can significantly reduce
the resources required for large-scale assessment of road features.
There are currently calls to develop 360-degree images as visual replicas of real landscapes as 360degree videos, which are widely used in research because they save time and are inexpensive. 360degree video, known as spherical video or immersive video, allows the user to view in all directions(34). 360-degree videos allow users to experience more realistic environments than photos, and whenviewing these videos using VR platforms, the user is immersed in the video and the interaction withthe real world is cut off (35-37). Images presented through 360-degree video methods are more
realistic than a commonly used 1D photo or virtual reality representations and jus t as easy to use. As
technology has developed; the use of 360-degree video as a research method has become more reliableand accessible; it has opened up new opportunities for research in the fields of environmental
psychology, architecture, urban design and urban planning (38-39). Moreover, many 360-degree
cameras store the soundscape as spatial sound for immersive video experiences. In this case, thesound; it is stored as a 4-channel audio data format called B-format and is performed using theAmbisonics framework in the playback process.
4.2. 3D Ambisonic audio-recording
Auralization techniques are often used to reproduce sound signals and simulate acoustic
transmission effects in real environments. It is defined as "a technique for generating audible so undfiles from digital (analog, measured or synthetic) data"(40).
Spatial audio is superior to stereo and surround sound formats because it provides users with amore immersive experience. Recently it has been widely used for auralization in soundscape res earch,and it has two advantages:• The first major advantage is the high ecological validity of the different soundscapes,
which gives the test subjects the perfect illusion of being "in the real place".
• The second major benefit is that immersive playback through VR allows evaluation ofsoundscapes under controlled laboratory conditions. This enables soundscape researchers
to isolate different stimuli to understand the independent contributions to the overallsoundscape perception (41).

5. Application steps of innovative approaches in an urban area
We carried out a recording procedure in an urban public park, in order to use a 360-degree cameraand 3D ambisonic sound recording, one of the innovative approaches.
5.1. Visual and audio recording setup
The methodology of recording is to use the device shown in Figure 2 for omnidirectional video
and Ambisonic audio recording. Insta360 Pro2 device was used for 360-degree video recording while
for sound recording it were used a SQobold – 4-channel sound recorder, a SoundField RØDE' NT-SF1
Ambisonic microphone and a 4-channel Soundfield sound recorder. The setup can be used for outdoor
recordings in noisy locations for recording sounds for a long duration. Moreover, the data recordedby the calibrated microphone gives the accurate SPL and can be used later for post processing tasks.

Figure 1: Experimental protocol for recordings

5.2. The Process of using audio-visual data-rendering
The captured audio and 360° video must be post-processed and rendered for making 360° video
with 3D ambisonic. Thus, the rendering process can be summarized in three steps, as shown in Figure
2.

Figure 2 : The complete reproduction process for 360-degree video and 3D Ambisonic audio.
The visual recordings from the Insta360 Pro2 camera are stitched using the insta360 stitcher
software. The spatial audio rendering is done using the software Reaper. Adobe Premiere Pipeline
software helps in synchronizing the audio along with the video recorded by the cameras. Since therecording is made with the help of an ambisonic microphone. The software and plugin allow theposition of virtual sound sources to be adjusted at different spatial locations for the playback.
Figure 3 (A) illustrates the noise map of the urban park evaluated with the actual traffic flows
conditions along the boundaries of the park. Detailed noise levels in the different sectors throughout
the urban park are reported. Based on this representation the local authorities could propose noise
mitigation actions to reduce noise levels. However, the information contained in the noise map is too
partial with respect to the human response. It does not take into account all environmental conditions
such as the presence of natural sound sources, the presence of masking sound sources, andmultisensorial responses. The classical noise map representation has been integrated in the fixed
positions 1, 2 and 3 in Figure 3 (B) with "360-degree video and 3D Ambisonic audio" recordings. The
citizens and the policy makers can have access to this data and can interact with the environment
through panoramic view and 3D Ambisonic audio experiences. They can report their global acceptancejudgement for each position. In this example points 1, 2 and 3, which have the same noise levels
according to noise map, are representative of different human responses.

Figure 3 : Example of the urban park: (A) classical noise map representation , (B) fixed positions
with 360-degree video and 3D Ambisonic audio recordings , (C) panoramic images.

6. Conclusion
Urban planning policies aim to improve the physical and mental health of residents, and the
combination of audiovisual information is considered an integral part of providing a sustainable
environment that enhances well-being.
The combination of audio-visual stimuli such as 360-degree videos and 3D ambisonic soundrecording, available on platform as integration of the classical noise maps, can contribute positively
to the understanding of environmental assessment and perceptual qualities and it can increase theengagement of the citizens and the awareness of the policy makers in the urban sound planning
processes.

References
1. United Nations—Department of Economic and Social Affairs. Available online: https://www.
un.org/development/desa/publications/2018-revision-of-world-urbanization-prospects.html (accessed on
19 July 2022).
2. Brambilla G, Pedrielli F. Smartphone-Based Participatory Soundscape Mapping for a More Sustainable
Acoustic Environment. Sustainability 2020; 12, 7899.
3. Bruce V, Green P R, Georgeson M A. Visual Perception: Physiology. Psychology and Ecology Psychology
Press; Hove & London, 1996.
4. Kang J, Aletta F, Gjestland T T, Brown L, Botteldooren D, Schulte-Fortkamp B, Lercher P, Van Kamp
I, Genuit K, Fiebig A, Coelho J L B, Maffei L, Lavia L. Ten questions on the soundscapes of the built
environment. Build. Environ. 2016, 108, 284–294.
5. Van Kamp I, Klæboe R, Brown A, Lercher P. Soundscapes, Human Restoration and Quality of Life. CRC
Press, Boca Raton, 2015.
6. Baclet S, Venkataraman S, Rumpler R, Billsjö R, Horvath J, Österlund P E. From strategic noise maps toreceiver-centric noise exposure sensitivity mapping, Transportation Research Part D: Transport andEnvironment, 2022; vol. 102, p. 103114.
7. Deng L, et al. Effects of integration between visual stimuli and auditory stimuli on restorative potential
and aesthetic preference in urban green spaces, Urban Forestry & Urban Greening, 2020; vol. 53, p.
126702.
8. Tarko J, Tompkin J, Richardt C, OmniMR: Omnidirectional Mixed Reality with Spatially-Varying

Environment Reflections from Moving 360° Video Cameras. 2019.
9. Afzal S, Chen J, Ramakrishnan K. Characterization of 360-degree Videos. 2017.
10. European Union, Directive 2002/49/EC of the European Parliament and the Council. Assessment andmanagement of environmental noise. Official Journal of the European Communities, L189/12-L189/25,
2002.
11. Lan Z, Cai M. Dynamic traffic noise maps based on noise monitoring and traffic speed data.
Transportation Research Part D 94, 2021.
12. Yang W, Kang J. Acoustic comfort evaluation in urban open public spaces, Applied Acoustics, 2005; 66,
211–229.
13. Alves S, Altreuther B, Scheuren J. Holistic concept for urban sound planning applied to real sites,
Proceedings of the Forum Acousticum Conference. Krakow, Poland 2014.
14. Maffei L, Di Gabriele M, Masullo M , Aletta F. On the perception of Limited Traffic Zones as urban noise
mitigation action, Noise Mapping, 2014; 1, 50–58.
15. Lavia L, Eastel M, Close D, Witchel H J, Axelsson Ö. Sounding Brighton: practical approaches towards
better Soundscapes. Proceedings of the Internoise Conference, New York, USA, 2012.
16. Eastel M, Bannister S, Kang J, Aletta F, Lavia F, Witchel H J. Urban Sound Planning in Brighton and
Hove, Proceedings of the Forum Acousticum Conference. Krakow, Poland 2014.
17. Raimbault M, Dubois D. Urban soundscapes: Experiences and knowledge, Cities, 2005; 22, 339–350.
18. Aletta F, Kang J. Soundscape approach integrating noise mapping techniques: a case study in Brighton,
UK, Noise Mapping, 2015; 2, 1-12.
19. Aumond P, Can A, Mallet V, De Coensel B, Ribeiro C, Botteldooren D, Lavandier C. Kriging-based
spatial interpolation from measurements for sound level mapping in urban areas. J. Acoust. Soc. Am.
2018; 5, 2847–2857
20. Picaut J, et. al. Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review,
Sensors, 2020; 20, 2256.
21. Hong J Y, He J, Lam B, R Gupta, Gan W S. Spatial Audio for Soundscape Design: Recording andReproduction. Applied Sciences, 2017.
22. ISO 12913-2:2018. Acoustics -- Soundscape -- Part 2: Data collection and reporting requirements. 2018
23. Lercher P, Schulte-Fortkamp B. The Relevance of Soundscape Research to the Assessment of Noise
Annoyance at the Community Level. In Proceedings of the 8th International Congress on Noise as a
Public Health Problem, Rotterdam, The Netherlands, 29 June–3 July 2003.
24. Schulte-Fortkamp B, Dubois D. Recent advances in soundscape research. Acta Acust. United Ac. 2006;
92, V–VIII.
25. Nilsson M E, Berglund B. Soundscape quality in suburban green areas and city parks. Acta Acust. United
Ac. 2006; 92, 903–911.
26. Brown A L. Areas of Hıgh Acoustıc Qualıty: Soundscape Plannıng, 14th International Congress on Sound& Vibration, Cairns, Australia. 9-12 July 2007.
27. Rezvanipour S, Hassan N, Ghaffarianhoseini A, Danaee M. Why does the perception of street matter? A
dimensional analysis of multisensory social and physical attributes shaping the perception of streets.
Archit. Sci. Rev. 2021; 64, 359–373.
28. Nitidara N, Sarwono J, Suprijanto S, Soelami F. The multisensory interaction between auditory, visual,and thermal to the overall comfort in public open space: A study in a tropical climate. Sustain. Cities Soc.
2022; 78, 103622.
29. Bratman G N, Hamilton J P, Daily G C. The impacts of nature experience on human cognitive function
and mental health. Ann. N. Y. Acad. Sci. 2012; 1249, 118–136.
30. Hägerhäll C, Taylor R, Cerwén G, Watts G, Van den Bosch M, Press D. Biological Mechanisms and
Neurophysiological Responses to Sensory Impact From Nature. Oxford University Press, Oxford. 2018.
31. Song X H , X B Lv, D M Yu, Q Q Wu. Spatial-temporal change analysis of plant soundscapes and theirdesign methods. Urban For. Urban Green, 2018; 29, 96–105.
32. Deng L et al., Effects of integration between visual stimuli and auditory stimuli on restorative potential
and aesthetic preference in urban green spaces. Urban Forestry & Urban Greening, 2020; vol. 53, p.
126702.
33. Griew P, Hillsdon M, Foster C, Coombes E, Jones A,P Wilkinson. Developing and testing a street audit
tool using Google Street View to measure environmental supportiveness for physical activity. Int J Behav
Nutr Phys Act, 2013; vol. 10, p. 103.
34. Elmezeny A, Edenhofer N, Wimmer J. Immersive storytelling in 360-degree videos: An analysis ofinterplay between narrative and technical immersion. Journal For Virtual Worlds Research, 2018; 11(1).

35. Baños R M, Botella C, Alcañiz M , Liaño V , Guerrero B, Rey B. Immersion and emotion: Their impact
on the sense of presence. Cyberpsychology & Behavior, 2004; 7(6), 734–741.
36. Portman M E, Natapov A, Fisher-Gewirtzman D. To go where no man has gone before: Virtual reality inarchitecture, landscape architecture and environmental planning. Computers, Environment and Urban
Systems, 2015; 54, 376–384.
37. Mouratidis K, Hassan R. Contemporary versus traditional styles in architecture and public space: A virtual
reality study with 360-degree videos. Cities, 2020; 97.
38. Sun K, De Coensel B, Filipan K, Aletta F, Van Renterghem T, De Pessemier T, Botteldooren D.
Classification of soundscapes of urban public open spaces. Landscape and Urban Planning, 2019; 189,
139–155.
39. Shimamura et al., Audio–visual object removal in 360-degree videos. The Visual Computer, 2020; 36,
2117–2128.
40. Vorländer M, Auralization. Springer-Verlag Berlin Heidelberg, 2008.
41. Gupta R et al., 3D Audio AR/VR Capture and Reproduction Setup For Auralization of Soundscapes. 2017.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0509

Mental mapping as a qualitative approach for identifying urban
soundscapesGeon-Hee KIM1; Joo-Young HONG2
1

Department of Architectural Engineering, Chungnam National University, Korea

2

Department of Architectural Engineering, Chungnam National University, Korea

ABSTRACT
A mental map is an effective tool for assessing a person's perception of their area of interaction. This study
aims to propose a mental mapping method for qualitatively assessing urban soundscapes. A case study area
including various urban soundscape qualities was selected in Daejeon, Korea. For the mental mapping
approach, the participants were asked to draw mental sketch maps that provide perceptions of individuals
regarding the sound and visual environments in the case study area with three layers. The first layer provided
an overall mental sketch that pivotal distance elements such as roads, rivers, etc. The second layer dealt withdetailed elements affecting the acoustic environment considering nodes, districts, and landmarks. The thirdlayer was about selecting the specific locations that were acoustically changed dramatically or uniquely.
Additionally, a questionnaire-based caption evaluation method was used to assess the components, traits, andperceptions of the aural environment. The findings of this study demonstrated that the mental mapping
method was capable of providing a wide range of subjective responses as well as identifying the perceived
elements of the urban soundscape.
Keywords: Mental map, Soundscape, Soundscape map, Perception, Qualitative approach

1. INTRODUCTION
The acoustic environment is one of the critical factors forming an overall impression of the urban
environments. Previous studies have shown that the perception of the environment is influenced not
only by visual elements but also by auditory elements (1,2). Studies on urban perception have mainly
focused on visual factors until now. Kevin Lynch published The Image of the City, one of the mostsignificant theories ever written about spatial cognition and behavioral geography. Studying the city
form, Kevin Lynch discovers that a community cognitive map can be created when people's mental
images intersect. The cognitive map is formed by five types of elements: paths, edges, nodes, districts ,and landmarks (3,4). However, in order to comprehensively assess the overall perceptions of urban
environments, it is also necessary to investigate how people perceive urban sound environments.
Currently, the importance of soundscape design has been emphasized to create a sustainable urban
street environment. According to ISO 12913-1, a soundscape is the “acoustic environment asperceived or experienced and/or understood by a person or people, in context”. Context is essential tothe analysis and design of soundscapes, as emphasized in ISO 12913-1. Three methods for collecting
soundscape data are specified in ISO 12913-2. Method A is a quantitative data collection method thatenables all response results as numeric data through a questionnaire. Method B combines characterand numeric data to collect data in both the quantitative and qualitative domains. Method C is aqualitative data collection method that uses a narrative interview to express responses in written
characters (5). Method A and B have limited to evaluate contextual factors of soundscapes beyond
1
2

ghkim@g.cnu.ac.kr
jyhong@cnu.ac.kr

structured survey items. Also, Method C has difficulties in providing comprehensive and systematic
analysis protocols for identifying contextual factors through a narrative interview method. Therefore,
this study aims to propose a comprehensive soundscape evaluation method to identify soundscape andcontextual factors using mental mapping.

2. Methods
2.1 Study area
As shown in Fig. 1, the case study area was chosen in Daejeon Yuseong-gu area. The case study
area includes residential areas and campus areas with educational buildings and facilities, which
Chungnam National University (CNU) students are mainly using in their daily lives. The case study
area was divided into four districts: Gung-dong (D1), Bongmyeong-dong (D2), Jangdae-dong (D3),and Juk-dong (D4).
The four districts were classified into two urban functions, residential areas and commercial areas,
to explore method of evaluating the soundscape using the mental map. Specifically, D1 is located in
the CNU Campus and commercial facilities. D2 is located in the commercial areas, while D3 and D4
are located residential areas.

Figure 1 – Study area and photos

2.2 Participants
Three participants were recruited to evaluate the perceptions of the acoustic environments in thecase study areas. All participants were CNU students who have lived near the case study area. The
age range of the participants was 20 to 29 years. In agreement with ethical procedures, the participants
were provided written information about the study, and written consent was obtained from all of theparticipants. Before they took part in the mental mapping, there was an introduction session to explain
the objective of this study and the mental mapping procedures with providing some examples ofmental maps in order to help the participants’ understanding of the mental mapping method.

2.3

Collecting data

2.3.1 Mental mapping procedure
Participants were asked to draw perceived acoustic environments with three layers of mental maps.
In the first layer, the participants were asked to draw path and edge in the case study area such as
roads, riversides, etc. The second layer dealt with landmarks, districts, and nodes. The participants
were asked to draw cognitive features in the case study area in order to assess the factors influencing
the visual perceptions of the study area. Also, the participants were asked to put annotations and labelson the cognitive features to provide information of the cognitive features (e.g., building names andlocations). The third layer provided mental maps related to soundscape elements. Participa nts wereasked to select specific locations that they noticed any unique, positive or negative characteristics ofthe soundscapes. Additionally, the acoustic quality of each element was evaluated based on three
categories: negative, positive, and soundmarks.

2.3.2 Caption evaluation
A caption evaluation method was adopted for collecting data on how the acoustic environments inthe case study area are perceived by participants. In the first session, demographic data of theparticipants were collected such as gender, age, departments, grades, and periods of residence in thecase study area. Second session was dealt with individual noise sensitivity (INS) of the participants.
The final section focused on how the acoustic environment was perceived.
The perceived acoustic environment was assessed using the nine attributes: location, date of use,time of use, purpose of visit, elements, characteristics, impressions, narrative evaluation, and overallsoundscape quality. A five-point was used to evaluate the overall soundscape quality scale (1: very
bad, 2: bad, 3: neither bad nor good, 4: good, 5: very good) (6).
2.4 Data analysis
The nine attributes evaluated by caption evaluation method were analyzed using text mining
technique. Text mining was performed to explore the relationships among the nine attributes. The
responses were tokenized and, in total, 303 datasets were obtained. The stopwords were removed fromthe dataset and the headword were extracted for text data preprocessing. Using the processed thedataset, we analyzed the type, frequency, and relationship among elements of the main words whichwere used for the soundscape evaluations.

3. RESULTS
This section consists of three subsections: 1) perceived acoustic and visual elements, 2) lexicalfrequency analysis, and 3) correlation among acoustic elements. In Section 3.1, the perceived visualand acoustic factors from the mental maps were analyzed. In Section 3.2, using the caption evaluation
responses, the frequency of terms was analyzed according to the overall soundscape quality. In Section
3.3, The phi-coefficient was used to calculate pairwise correlation for the cluster words. (7)3.1 Perceived acoustic and visual elements
Fig. 2 shows three mental maps created by the participants. Regarding the path and edge elementsin the first layer, several elements were selected in common such as Yuseongcheon stream, major road
(10-lanes) in front of CNU Main Gate, Gung-dong Rodeo Street, and Jangdae-dong crossroads. In
terms of the second layer, Jangdae Elementary School, Jangdae Middle School, Engineering college
building 2, CNU Main Gate, CNU Library were commonly selected as landmarks in the study area.
In general, there were many negative elements near intersection, main roads or commercial areas,
whereas public spaces such as the riverside, park or residential complexes located in Juk -dong or
Jangdae-dong were evaluated as positive elements.

Figure 2 – CNU Student’s mental maps

3.2 Sentiment Analysis
Following corpus tuning, summary information for the text was collected using the tm and tidytext
packages in text mining. The frequency of a term in the corpus or in a particular document can be
determined by term frequency analysis. Consequently, how a term is important can be understood.
The high-frequency term used in the assessment of the acoustic environment is shown in Fig. 3.
Relatively important words for positive and negative evaluations were extracted using odds ratio. The
most popular vocabulary used positive evaluation was 'river', 'Yuseongcheon', 'comfortable', 'live', and'Jukdong'. The words used most frequently in the negative evaluation was 'noisy', 'car', 'Gungdong','Bongmyeong-dong', 'road'.

Figure 3 – Highest positive and negative scoring sentiment words in the dataset

3.3 Correlation
The study also conducted correlation analysis. The range of phi coefficient ranges from -1 to 1,
meaning how many terms are used together compared to when they are used separately. Interpretingcorrelations displays the relationship between two terms.
In this study, representative evaluation vocabulary was set through frequency analysis. By
examining correlations, terms having higher relation with the term "sound”, “people" and "CNU" canbe appeared as "CNU, "talk", and "sound", respectively. Conversely, the terms with lower relevancecan be expressed in the order of "lot", "river", and "Gung-dong".

Figure 4 –Pairwise correlation of the main cluster terms

4. CONCLUSIONSThis study aims at utilizing mental mapping method to propose for qualitatively evaluating urban
soundscapes. The elements of the acoustic environment perceived by the participants showed similarevaluations at some points. The findings suggest that the perceived 'path' or 'edge' elements could be
one of the important contexts for considering urban soundscapes.
This study also has shown that mental mapping could be an alternative method to identify various
qualitative factors affecting soundscapes. Despite these findings, there remain several limitations
inherent in this study. First, data collected from participants might be a limitation due to a significant
shortage of participants. Additionally, in the future, the relationship between soundscape andlandscape factors will be investigated.

ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by
the Korea government (MSIT) [No. 2022R1C1C1008066]

REFERENCES
1. Hong JY, Jeon JY. Comparing associations among sound sources, human behaviors, and soundscapes on
central business and commercial streets in Seoul, Korea. Build Environ [Internet].
2020;186(May):107327. Available from: https://doi.org/10.1016/j.buildenv.2020.107327
2. Meng Q, Sun Y, Kang J. Effect of temporary open-air markets on the sound environment and acoustic
perception based on the crowd density characteristics. Sci Total Environ [Internet]. 2017;601–602:1488–
95. Available from: http://dx.doi.org/10.1016/j.scitotenv.2017.06.017
3. Filomena G, Verstegen JA, Manley E. A computational approach to ‘The Image of the City.’ Cities[Internet]. 2019;89(August 2018):14–25. Available from: https://doi.org/10.1016/j.cities.2019.01.006
4. Gieseking JJ. Where We Go From Here: The Mental Sketch Mapping Method and Its Analytic
Components. Qual Inq. 2013;19(9):712–24.

5. Jo HI, Jeon JY. Compatibility of quantitative and qualitative data-collection protocols for urban
soundscape evaluation. Sustain Cities Soc [Internet]. 2021;74(November 2020):103259. Available from:https://doi.org/10.1016/j.scs.2021.103259
6. Naoi T, Yamada T, Iijima S, Kumazawa T. Applying the caption evaluation method to studies of visitors’
evaluation of historical districts. Tour Manag [Internet]. 2011;32(5):1061–74. Available from:http://dx.doi.org/10.1016/j.tourman.2010.09.005
7. Basu A, Hazra AK, Chaudhury S, Ross AB, Balachandran S. State of the art research on sustainable useof water hyacinth: A bibliometric and text mining analysis. Informatics. 2021;8(2).

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0270

Acoustic Comfort and Sound Preference in the Nursing Home for
Blind Older Adults
Sijia Huo1; Yue Wu1*; Jian Kang2; Qi Meng1, Fangfang Liu1; Da Yang1
1

Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology ,Ministry of Industry and Information Technology , School of Architecture, Harbin Institute of Technology, China
2

UCL Institute for Environmental Design and Engineering, United Kingdom

ABSTRACT
There have been many studies exploring the perception of the acoustic environment in older adults, but doblind older adults make a difference? This study conducted a subjective evaluation of the acoustic
environment in an all-blind nursing home in China, investigating acoustic comfort and sound preference fordifferent types of sound sources. The study found that 36.7% of the blind older adults were highly satisfied
with the acoustic environment in nursing homes; blind older adults preferred natural sound and informationalsounds and were more receptive and dependent on artificial sounds than others; the results indicated thatblind older adults with light perception were more sensitive to sound and preferred an audible environment
than blind older adults with no light perception, and they needed the feeling of having someone around them.
This study may help nursing homes to provide environmental support for blind older adults.
Keywords: Blind older adults, Acoustic comfort, Sound preference

1. INTRODUCTION
Noise can cause not only mental health problems such as irritability, insomnia and depression, butalso physical health problems such as tinnitus, dizziness, heart disease and cognitive impairment. In
1794, Miller systematically reviewed the effects of noise on people and suggested that older adults
need a less noisy environment for both communication and sleep(1). In addition, age -related hearing
loss causes communication problems with approximately 37% of people aged 61 to 70 and 60% of
people aged 71 to 80(2). People can perceive space through their bodies without using their vision in
the process of movement(4). The sense of hearing can convey external information and identify thesurrounding environment, and blind people outperform normal people in speech perception, auditory
memory, sound source localization and speaker recognition(5). Chen summarized the ways in whichblind people perceive the external world through auditory perception, suggesting that sound not only
directly conveys linguistic information, but is also a major vehicle for entertainment and can help
them orient, remember and perceive things, a complement to the absence of vision(6). All of thesestudies have highlighted the impact and importance of sound on the elderly and blind, but few erstudies have considered the acoustic perception of blind older adults.
It is a case study for the only nursing home in China that provides environmental and service
support for the blind older adults. It investigated the perception of blind older adults in the nursing
home of different types of sound sources in terms of both acoustic environment comfort and soundsource preference, with a view to helping nursing homes provide environmental support for blindolder adults.

1

wuyuehit@hit.edu.cn

2. METHODOLOGY
2.1 Survey Site
This all-blind nursing home is located in Shenyang, Liaoning Province, Northeast China. The home
has five floors, 96 care units, approximately 30-50 elderly residents and 12 staff. The types of soundsources in the home are summarized according to the site loca tion, building layout and user behavior.
Inside the building, there are the sounds of talking and walking between blind older adults, the activity
sound generated by staff and blind older adults, the prompt sound used by the elevator to indicate thefloor and broadcast the notice of dining and other events, as well as the mechanical sound generated
by the equipment supporting the operation of the nursing home and household appliances for blindolder adults. Outside the building there is talking sound, activ ity sound and broadcast sound to help
orient blind older adults, as well as traffic and nature sounds.
2.2 Questionnaire Survey
In this study, the subjective evaluation questionnaire was divided into two parts: backgroundinformation survey and subjective evaluation, and participants' attitudes were measured using a 5point Likert scale from 1 to 5, as shown in Table 1. Many previous studies of the acoustic environment
have paid particular attention to acoustic comfort(7,8,9,10) and sound preference
assessment(11,12,13) and explored the influence of some factors, so the subjective evaluation in thisstudy explored these aspects as well.
Table 1 - Questionnaire and scales
Category
Questions
Scale
Background
Gender; age; light perception; length of
information
residence
Acoustic
Comfort of the acoustic environment
1 being very uncomfortable to 5
evaluation
being very comfortable
Preference degree of various sound1 being highly disliked to 5 being
sourceshighly liked
2.3 Participants
In this study, questionnaires were distributed among the field three times in July, October andDecember 2021, the participants were the blind older adults living in the home, the staff and thestudents and teachers that took part in the survey. 90 questionnaires were distributed and 89 valid
questionnaires were returned, including 60 from the blind older adults and 29 from others, the basic
profile of the respondents is shown in Table 2. The reliability coefficient of the questionnaire was 0.83
(Cronbach's alpha).
Table 2 - Respondent Profiles.
MeasuresItems
blind older adults Other people All respondents
Light perception
light perception
26
26
No light perception 34
34
Gender
Male
26
12
38
Female
34
17
51
Age
6024
28
52
60~69
13
1
14
70+
23
0
23
Duration of residence 3 months81422
3 months~6 months 11213
6 months to 1 year 15
621
1 year+26
733
2.4 Statistical Analyses
The questionnaire data were analyzed with SPSS 25(14). Descriptive statistics were used todetermine the distribution of participants' ratings of acoustic comfort and sound preference.
Independent sample t-tests were used to determine differences in participants' perceptions of acoustic
comfort and the effect of light perception and gender differences on the degree of sound preference.

One-Way ANOVA was used to determine the effect of the blind older adults' age and length of stayon sound preference.

3. RESULTS AND DISCUSSION
3.1 Comfort of the acoustic environment
As shown in Figure 1(a), a total of 78.4% of the blind older adults gave positive ratings to theacoustic environment of the nursing home, with 41.7% of them saying that the acoustic environment
was relatively comfortable and 36.7% found it very comfortable. 66.0% of the older respondents toZeng's survey on the acoustic environment of elderly living chose "very good" or "good"(15). Zhan
et al. found that 71% of the older adults felt that the acoustic environment in their nursing home was
acceptable (acceptable, unacceptable) when they surveyed the indoor air quality of the nursing
home(16).
In addition, the blind older adults rated the acoustic environment of the nursing home with a meanscore of 4.08 (±0.926), while the other participants rated it with a mean score of 3.34 (±1.495), as
shown in Figure 1(b). After an independent samples t-test, blind older adults rated the acoustic
environment significantly higher than others in the nursing home (p=0.019). Li found that the older
adults rated the acoustic environment with a mean score of 3.37 (±1.120) when investigating theacoustic preferences of the older adults in nursing homes(17). The acoustic comfort of the nursing
home investigated by Cui et al. was 3.36 (±1.120)(18). In conclusion, both young and old people rated
the acoustic comfort in nursing homes or living environments lower than blind older adults, which
may indicate blind older adults are more receptive and accepting of the acoustic environm ent.

Figure 1 – (a) The percentage of acoustic comfort evaluation for different people; (b) Acoustic comfort
evaluation scores for different groups of people (*p-value < 0.05.)
3.2 Sound preference
Figure 3 shows the preference of the blind older adults for various sound sources in the nursing
home. For indoor sounds, 52% of the blind older adults selected "dislike" or "highly dislike"mechanical sound; while most of blind older adults were neutral about the sound of activity andwalking. Some participants stated: "We need to hear the voices of others to avoid feelings of isolation".
In addition, 45% of the blind older adults "like" or "highly like" talking sound, while only 13% of theblind older adults "dislike" or "highly dislike" Prompt sound. For outdoor sounds, 55% of the blindolder adults chose "dislike" or "highly dislike" traffic sound, while they also hold a neutral attitude toactivity sound and talking sound; 83% and 92% of the blind older adults chose " like" or "highly like"broadcast sound and nature sound respectively.
In general, blind older adults like natural sound bests, followed by informational sounds such as
prompt and broadcast, which is consistent with some previous soundscape studies(19,20). Althoughsome studies have shown that many older adults would like to reduce the amount of talking andactivity sounds in elderly facilities(21), blind older adults are more neutral towards artificial sounds
such as talking, walking and activity sounds, which may indicate that they are more accepting anddependent on these types of sounds than others. Additionally, blind older adults dislike traffic soundand mechanical sound, which is consistent with other findings on sound preference(13,1 7,22).

Figure 3 - Evaluation of sound preference for blind older adults
3.3 Factors influencing sound preference
As shown in Table 3, the gender, age and duration of residence of the blind older adults did nothave a significant effect on their sound preference, while there was a significant difference in soundpreference between the blind older adults with light perception and the blind older adults with no lightperception.
In an independent sample t-test, the blind older adults with no light perception showed asignificantly higher preference for indoor walking sounds and outdoor talking, activity and naturesounds than the blind older adults with light perception. In terms of preferences for walking, talking
and activity sounds, the blind older adults with no light perception preferred artificial sounds, possibly
because they need more reassurance through artificial sounds that they are in a social environment. In
addition, although the difference in preference for other sounds was not significant, i t can be seenfrom Figure 4 that the mean values of preference for other sounds were higher for the blind older
adults with no light perception, except for broadcast sounds. It indicates that the blind older adults
with no light perception are more sensitive to sound and prefer an audible environment.
Table 3 - Significance of different factor categories on sound preference
Preference degree of indoor sound sources
Sound sources
Talking
Walking
Activity
PromptMachineFactors
sound
sound
sound
sound
sound
Light perception
0.217
0.048*
0.156
0.260
0.529
Gender
0.465
0.903
0.675
0.529
0.991
Age
0.587
0.296
0.688
0.929
0.392
Length of residence
0.848
0.771
0.098
0.817
0.433
Preference degree of outdoor sound sources
Sound sources
Talking
Activity
BroadcastTraffic
NaturalFactors
sound
sound
sound
sound
sound
Light perception
0.004**
0.014*
0.359
0.660
0.022*
Gender
0.744
0.454
0.533
0.817
0.159
Age
0.746
0.879
0.507
0.681
0.722
Duration of residence
0.982
0.956
0.346
0.727
0.102
note：*p-value < 0.05; **p-value < 0.01

note：*p-value < 0.05; **p-value < 0.01
Figure 4 - The relationship between sound preference and light perception

4. CONCLUSIONSThis study investigated the acoustic comfort and sound preferences of the only all -blind nursing
home in China. 78.4% of the blind older adults gave positive ratings of the acoustic environment of
the nursing home, of which 36.7% thought it is very comfortable, the blind older adults had a higher
level of acceptance and approval of the acoustic environment. In addition, they preferred natural andinformational sounds, dislike traffic and machine sounds, and have a higher acceptance of artificial
sounds than others. The blind older adults with no light perception are more sensitive to soun d andprefer an audible environment, they need sound to be sure that they are in a social environment. Staff
should reasonably arrange rooms according to the situation of blind older adults, so as to meet theneeds and expectations of blind older adults in an acoustic environment that is conducive to their lives
and health.

ACKNOWLEDGEMENTS
We are grateful to the blind older adults who participated in this study and to the staff who helped
us survey the nursing home and reach out to the participants. This study was supported by the China
Association of the Blind Group Standard Project: Construction of Blind Care Units in Elderly Car e
Facilities (No. T/CAPVD 0005-2021).

REFERENCES
1. Miller J D. Effects of noise on people[J]. The Journal of the Acoustical Society of America, 1974,
56(3):729-764.
2. Baur M, Fransen E, Tropitzsch A, et al. Einfluss exogener Faktoren auf Altersschwerhörigkeit[J]. HNO,
2009, 57(10): 1023-1028.
3. Oh Y K, Ryu J K. Acoustic design guidelines for houses for hearing impaired seniors–In the frameworkof Korean building codes[J]. Indoor and Built Environment, 2020, 29(3): 343-354.
4. Huang Y R. A study on the construction of an accessible environment for the visually impaired inbuildings[J]. Architectural Journal, 2006(56):1-26. (in Chinese)
5. Xu L L, Zhao X Y. Auditory strengths, neural mechanisms and educational insights for the blind[J].
Journal of Suihua College, 2018, 38(01):84-89. (in Chinese)
6. Chen H Y. Non-visual depth perception - a design study for the blind[J]. Art Journal, 2008(04):62-66. (inChinese)
7. Tarlao C, Fernandez P, Frissen I, et al. Influence of sound level on diners’ perceptions and behavior in a
montreal restaurant[J]. Applied Acoustics, 2021, 174: 107772.
8. Yang W, Kang J. Acoustic comfort evaluation in urban open public spaces[J]. Applied acoustics, 2005,
66(2): 211-229.
9. Meng Q, Kang J. Effect of sound-related activities on human behaviours and acoustic comfort in urban
open spaces[J]. Science of the total environment, 2016, 573: 481-493.
10. Wu Y, Kang J, Zheng W, et al. Acoustic comfort in large railway stations[J]. Applied Acoustics, 2020,
160: 107137.
11. Yu L, Kang J. Factors influencing the sound preference in urban open spaces[J]. Applied Acoustics, 2010,
71(7): 622-633.
12. Ismail M R. Sound preferences of the dense urban environment: Soundscape of Cairo[J]. Frontiers ofArchitectural Research, 2014, 3(1): 55-68.
13. Yang W, Kang J. Soundscape and sound preferences in urban squares: a case study in Sheffield[J]. Journalof urban design, 2005, 10(1): 61-80.
14. Green S B, Salkind N J, Green S B. Using SPSS for Windows and Macintosh: Analyzing andunderstanding data[J]. 2005.
15. Zeng Y J. The effect of living acoustical environment on the speech communication of the elderly
people[D]. South China University of Technology, 2018. (in Chinese)
16. Zhan H, Yu J, Yu R. Assessment of older adults’ acceptance of IEQ in nursing homes using both subjective
and objective methods[J]. Building and Environment, 2021, 203: 108063.
17.Li Z Z. Research on sound environment and sound preference of the elderly people in the nursing home[D].
Harbin Institute of Technology, 2016. (in Chinese)
18. Cui P, Zhang J, Li T T. Research on Acoustic Environment in the Building of Nursing Homes Based on
Sound Preference of the Elderly People: A Case Study in Harbin, China[J]. Frontiers in psychology, 2021:
4546.
19. Porteous J D, Mastin J F. Soundscape[J]. Journal of Architectural and Planning Research, 1985: 169-186.
20. Zhang D, Zhang M, Liu D, et al. Sounds and sound preferences in Han Buddhist temples[J]. Building andEnvironment, 2018, 142: 58-69.
21. Wang L, Kang J. Acoustic demands and influencing factors in facilities for the elderly[J]. Applied
Acoustics, 2020, 170: 107470.
22. Tamura A. An environmental index based on inhabitants’ recognition of sounds[C]//Proceedings of the
7th International Congress on Noise as a Public Health Problem. Sydney, Australia: International
Commission on Biological Effects of Noise, 1998.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0575

Effects of noticeability of birdsong sound on annoyance due toresidential noise in apartment
Songmi LEE1; Chunwon EOM2; Jongkwan RYU3
Chonnam National University, Republic of Korea

ABSTRACTThis study investigated the effect of the noticeability of birdsong sounds on annoyance due to residential
noise through auditory experiments. Residential noises in an actual apartment building and several birdsong
sounds were used. The sound source for auditory experiment was selected according to frequency andtemporal characteristics. In the experiment, participants rated annoyance for single residential noise sources,
single birdsongs, and combined sounds (residential noise + birdsong), respectively. For the combined sounds,
they also evaluated whether birdsongs were detected. It was found that the difference in frequency and soundpressure level between the frequency bands having the maximum sound pressure level of the residential noise
source and the birdsong was found to influence noticeability, which is related with annoyance.
Keywords: residential noise, birdsong, noticeability

1. INTRODUCTION
Various residential noise sources are generated in apartment houses, and complaints about floor impact
noise from upper houses are steadily increasing in Korea. To solve this problem, the government andconstruction companies are making efforts to reduce noise in residential spaces and sound insulation
performance of buildings. However, these solutions have limitations, and a psychological approach is needed
to create a more calm and comfortable living space.
There have been many studies on soundscape improvement using natural sounds for traffic noise andconstruction noise, and bird sounds were mainly used among various natural sounds [1-5]. However, most of
the birdsong with low low-frequency sound energy are difficult to energetically mask residential noise
sources with large low-frequency energy. Nevertheless, several studies found that birdsong sound contributes
enhancement in sound scape quality and perceived loudness [2-5]. A recent study [6] suggested that perceived
birdsongs is significantly impacted by frequency contents. In the author's previous study [7], it was foundthat the frequency band with the maximum sound pressure level of birdsong was correlated with the reductionof annoyance of residential noise sources.
In this study, we investigated how the perception of birdsong mixed with the residential noise source
affects the annoyance caused by the residential noise.

2. EXPERIMENT
2.1

Stimuli Source

Residential noise sources were recorded in real living spaces, and sound sources to be used in theexperiment were selected through k-means clustering. First, the frequency and time fluctuation characteristics
1
2
3

smi4879@naver.com
ucw1472@naver.com

jkryu@jun.ac.kr

of each sound source were analyzed through the AS-70 program (Rion). Based on the analyzed data, a totalof three clusters were divided according to frequency characteristics through k-mean clustering, and one
sound source most similar to the mean value of each cluster was selected. Meanwhile, 18 types of birdsong
sound sources were collected from the National Institute of Ecology in Korea, and all the collected bird
sounds were set at the same sound pressure level (L Aeq 60 dBA). Figure 1 shows the frequency characteristics
of birdsong, and the frequency band in which the maximum sound pressure level of birdsong appears is
widely distributed from 630 Hz to 5k Hz in 1/3 octave band. Three residential noise sources (child running,
outdoor unit, plumbing) were used in the experiment. In the case of birdsong, the sound sources were selectedby referring to author’s previous study [7]. The main experimental variables in this study are the octave band
difference (f) and the sound pressure level difference (L) between the frequency bands with the maximum
level of birdsong and residential noise, as shown in Figure 2. Experimental sound sources with variations inexperimental variables (f and L) were produced through sound software editing for each birdsong soundsource.

Figure 1 – Spectrum of birdsong sounds

Figure 2 – Experimental variables in this study

2.2 Experimental design
The method of limitation, one of psychometric methods, was used to investigate the threshold values of
two experimental variables (f and L) in terms of the noticeability of birdsong sounds mixed withresidential noise. In the experiment, the detection of bird sounds and overall annoyance were evaluated while
listening to a sound source mixed with residential noise and bird sounds. Experimental sessions were divided
according to the sound source presented. First, annoyance evaluation was conducted for single residential
noise and birdsong sound. Then, the sound source with the varied level of the experimental variable (f andL) was evaluated. Prior to the experiment, the participants sat in chairs and had them read the provided
magazines or books from their own selection. In addition, a quiet environment was maintained by monitoring
the noise level and situation during the experiment. The experiment was conducted in a listening room (H⨯W:
2.7 m⨯4.4 m=11.9 m2) with background noise of 15 dBA and reverberation time of 0.19 seconds (500 Hz
and 1k Hz average). The sound source of the experiment was presented to the participants through a woofer
speaker (Dynaudio BM14S) and a headset (Sennheiser HD 600).

3. RESULTS & CONCLUSION
In the case of a single residential noise source, it was found that the sound source with a large soundpressure level in the low frequency band was more annoyed. In addition, the sound of birdsong with thehigher dominant frequency band showed a higher annoyance score. The difference in frequency and soundpressure level between the frequency bands having the maximum sound pressure level of the residential noise
source and the birdsong was found to influence noticeability, which is related with annoyance. Results of thisstudy can be applied to the design of an indoor soundscape for a pleasant sound environment in a residential
space.

ACKNOWLEDGEMENTS
This work is supported by the Korean Agency for Infrastructure Technology Advancement (KAIA) grant
funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C163631-01).

REFERENCES[1] Best, V., Ozmeral, E., Gallun, F. J., Sen, K., & Shinn-Cunningham, B. G. (2005). Spatial unmasking ofbirdsong in human listeners: Energetic and informational factors. The Journal of the Acoustical Societyof America, 118(6), 3766-3773.
[2] Coensel, B. D., Vanwetswinkel, S., & Botteldooren, D. (2011). Effects of natural sounds on the perceptionof road traffic noise. The Journal of the Acoustical Society of America, 129(4), EL148-EL153.
[3] Hao, Y., Kang, J., & Wörtche, H. (2016). Assessment of the masking effects of birdsong on the road traffic
noise environment. The journal of the Acoustical Society of America, 140(2), 978-987.
[4] Dooling, R. J., & Popper, A. N. (2016, July). Some lessons from the effects of highway noise on birds.
In Proceedings of Meetings on Acoustics 4ENAL (Vol. 27, No. 1, p. 010004). Acoustical Society ofAmerica.
[5] Hong, J. Y., Ong, Z. T., Lam, B., Ooi, K., Gan, W. S., Kang, J., ... & Tan, S. T. (2020). Effects of adding
natural sounds to urban noises on the perceived loudness of noise and soundscape quality. Science of the
Total Environment, 711, 134571.
[6] Hong, X. C., Wang, G. Y., Liu, J., Song, L., & Wu, E. T. (2021). Modeling the impact of soundscapedrivers on perceived birdsongs in urban forests. Journal of Cleaner Production, 292, 125315.
[7] Lee S., Eom C., & Ryu J. (2022). Sound masking of residential noise by a birdsong depending on spectral
and temporal characteristics. Proceeding of Internoise 2022.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0088

Noise annoyance reduction by green window view : How muchgreenery do we need?
Timothy VAN RENTERGHEM1; Elin VERMANDERE1; Willem BEKERS2
1

Ghent University, Department of Information Technology, WAVES research group, Belgium
2Ghent University, Department of Architecture and Urban Planning, Belgium

ABSTRACT
There is evidence that seeing outdoor vegetation through the windows of one’s dwelling is able to mitigate
negative health effects due to exposure to environmental noise, in particular for noise annoyance. However,design guidelines to maximally benefit from this positive audio-visual interaction are currently lacking, but
are mandatory when this idea is to be considered in (holistic) urban sound planning. In a virtual reality
experiment, mimicking an existing situation along the inner city ring road of Ghent (Belgium) at high noise
exposure levels, a test panel (n=40) was exposed to varying tree densities at a central reservation. Preliminary
analysis showed that the optimum green window view was near 30% (in terms of RGB greenness). Given theinherent large variation in self-reported noise annoyance due to road traffic noise, this minimum washowever not very pronounced. In contrast, the near absence of vegetation (10%), or vegetation that is too
dense (50%), lead to higher self-reported noise annoyance.
Keywords: Noise annoyance, Audio-visual interactions, Environmental perception, Urban sound, Virtual
Reality

1. INTRODUCTION
There is evidence that seeing outdoor vegetation through the windows of one’s dwel ling is able tomitigate negative health effects due to exposure to environmental noise, in particular for noise
annoyance.
Li et al. (2010) showed by their surveys held in Hong Kong that visible outdoor greenery reduces
self-reported noise annoyance for residents of high-rise buildings overlooking urban parks andwetlands. The category “a lot of greenery, parks and gardens” lead to a 2-point shift towards lessannoyance (on an eleven-point scale) when compared to “no greenery”.
Outdoor vegetation as seen from the living room facing the highly noise-exposed inner-city ringroad of Ghent showed to be a strong predictor of self-reported noise annoyance (Van Renterghem andBotteldooren, 2016). No view on vegetation resulted in a 34% chance of being at least mod erately
annoyed by road traffic noise, while this chance reduced to 8% for respondents having a “pronounced”
vegetation view.
Another Hong Kong study, by Leung et al. (2017), assessed the effect of combinations ofenvironmental features. The probability of high annoyance when viewing noise walls was 26%, whilewith vision on greenery this percentage reduced to only 5%.
These studies indicate that audio-visual interactions play an important role when people perceive
their environments. As discussed in more detail by Van Renterghem (2019), the well-known stress
reduction people experience when looking at vegetation (see e.g. Ulrich et al., 1991) could explain thenoise annoyance alleviation.
However, design guidelines to maximally benefit from this positive audio-visual interaction when
citizens perceive their environments are currently lacking. Quantitative goals are mandatory when thisidea is to be included in the urban sound planning toolbox.

1

Contact : timothy.vanrenterghem@ugent.be2. METHODOLOGY
2.1 Virtual Reality Environment
The virtual environment was a living room at the first floor of a terraced house, looking at a road (2times 2 lanes) with dense but freely flowing traffic. The vegetation was located along a wide(ned)central reservation containing trees at five densities. The RGB greenness parameter was used toquantify the vegetation, and the fraction of greenish pixels in the window pane ranged from 10% to
50% (see Fig. 1). The 3D modelling was performed with Rhinoceros, while Twinmotion was used forthe rendering.

Figure 1 – Vegetation scenarios as used in the current audio-visual perception experiment. An increasing
vegetation density is modeled when going from left (10% : no trees, only grass) to right (50% : opaqueextreme vegetation scenario).

2.2 Sound Reproduction
Binaural recordings were made inside a real-life dwelling on which the modeled VR environment
was based. A Head-and-torso simulator (HATS) was positioned (frontal view) at close distance from ahalf-open window. The recordings (dominant road traffic noise and near absence of other types ofsounds) were appropriately filtered to have an identical exposure at headphones used during theperception test in the lab. The equivalent sound pressure level, averaged over both ears, was equal to
67 dBA. Although the participants were allowed to look around in the virtual living room, theirposition was fixed (close to the window, as for the HATS as shown in Fig. 2) preventing level
differences as would be observed when moving away from the window. Directional sound was notconsidered.

Figure 2 – HATS measurement as basis for the frontal view road traffic sound reproduction in the VRexperiment.

2.3 Test panel
Forty persons participated in the audio-visual experiment. To ensure people were looking throughthe window in the VR environment most of the time, they were asked to count the number of bicycles
driving along the central reservation which can be considered as a light cognitive task (see Fig. 3).

Participants were exposed to each of the 5 vegetation scenarios ( in randomized order) for 5 minutes,
after which they answered a few questions, among which a standard noise annoyance question (on ascale from 1 to 11). The sound recordings were each time very similar (but not identical) and fixed at
67 dBA LEQ. After having evaluated all scenarios, a more extensive set of questions wereadministered, asking for personal data and gathering information about their living environment,
followed by a noise sensitivity questionnaire (Weinstein, 1980), a connectedness-to-nature (Mayer andFrantz, 2004) questionnaire, and an assessment of their current stress status (Cohen et al, 1983).

Figure 3 – Snapshot of moving vehicles including the bicyclers the participants were asked to count along thecentral reservation.

3. RESULTS
3.1 Dichotomous noise annoyance in function of green percentage
Descriptive statistics show a U-shaped curve when plotting green percentage vs self-reported mean
absolute noise annoyance rating. Given the high (natural) variability in noise annoyance ratings andthe limited number of participants, data were first pooled to perform meaningful statistics.
The noise annoyance rating was transformed to a dichotomous variable by a median split, showing
differences (at the 5% statistical significance level, Wilcoxon signed-rank test) between the 50% green
scenario and all other scenarios. Between 10+20+40+50% vs 30%, between 10+50% vs 30%, andbetween 20+30+40% vs 10+50%, less strong differences are found (at the 20% statistical significancelevel). So the most dense scenario induces the highest self-reported noise annoyance, while scenario
30% shows a tendency to be different from the others, indicating a minimum in noise annoyance.
3.2 Noise annoyance prediction in function of green percentage with machine learning
In a alternative approach, an artificial neural network (ann) was trained on the data to catch
potentially complex non-linear relations between all variables, without any a priori assumptions on thedata (e.g. no presets on distributions and allowance for a mixture of variable types). To have an
indication of the impact of (randomly) choosing a training, validation and test set during the ann model
development, 50 models were constructed, where the final outcome is the average of all models. Thisapproach allows to visualize 95% confidence intervals on the ann prediction.
In Fig. 4, a minimum in noise annoyance is seen at 30% greenery, while the absence of trees, or too
dense vegetation (50%), is less interesting. All other (personal) variables involved were then set attheir mean value over the test panel when evaluating the ann model. These findings are consistent withthe analysis in Section 3.1.

Figure 4 – Artificial neural network modeled absolute noise annoyance rating vs green percentage (full line).
The dashed lines indicate 95% confidence intervals on repeated model developments.
The ann model further showed some logical relationships e.g. between self-reported noise
sensitivity and self-reported noise annoyance (more noise sensitive people scoring higher) and e.g.
persons with a higher stress level being more noise annoyed during their immersion in the virtual
reality environment. Such basic findings give confidence in the non-standard machine learning
approach for data analysis and the perception experiment overall. Another interesting finding is that
persons scoring high on the connectedness-to-nature scale showed a tendency towards higher noise
annoyance.

4. CONCLUSIONS AND OUTLOOKThe audio-visual virtual reality experiment (n=40) conducted shows a minimum in self-reported
noise annoyance, although not very pronounced, at 30% greenery as seen through the dwelling’s
window pane. Both a very limited amount of greenery, or extremely dense vegetation, lead to a higher
noise annoyance reaction, regardless of the actual sound exposure level which was fixed at 67 dBA.
Further analysis should reveal to what extent personal factors have an influence on this minimum. The
creation of such guidelines are further complicated by many other design variables when, for instance,
the life cycle of greenery or the dynamic position of observers are also taken into account.

REFERENCES
1. Li H, Chau C, Tang S. Can surrounding greenery reduce noise annoyance at home? Science of the Total
Environment 2010;408:4376–4384.
2. Van Renterghem T, Botteldooren D. View on outdoor vegetation reduces noise annoyance for dwellers
near busy roads. Landscape and Urban Planning 2016;148:203-215.
3. Leung T, Xu J, Chau C, Tang S, Pun-Cheng L. The effects of neighborhood views containing multiple
environmental features on road traffic noise perception at dwellings. Journal of the Acoustical Societyof America 2017;141:2399-2407.
4. Van Renterghem T. Towards explaining the positive effect of vegetation on the perception ofenvironmental noise. Urban Forestry and Urban Greening 2019;40:133-144.
5. Ulrich R, Simons R, Losito B, Fiorito E, Miles M, Zelson M. Stress recovery during exposure to naturaland urban environments. Journal of Environmental Psychology 1991;11:201-230.
6. Weinstein N. Individual differences in critical tendencies and noise annoyance. Journal of Sound andVibration 1980;68:241–248.
7. Mayer F, Frantz C. The connectedness to nature scale: A measure of individuals’ feeling in community
with nature. Journal of Environmental Psychology 2004;24:503-515.
8. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. Journal of Health andSocial Behavior 1983;24:385–396.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0430

The influences of vegetation and water features on the perceptionof urban streetsaNazmiye Gulenay Yilmaz ; Pyoung-Jik Leeaa

Acoustics Research Unit, School of Architecture, University of Liverpool, Liverpool, UK

Anne HeimesbbInstitute for Hearing Technology and Acoustics, RWTH Aachen University, Aachen, Germany

ABSTRACTThis study aims to examine the influence of natural sound (i.e. water sound and birdsong) on the perceptionof the enclosure in urban street canyons with varying height-to-width ratios (H/W). A 27- meter wide street
was selected as a typical urban street and three levels of H/W ratios (0.5, 2, and 4) were modelled by changing
the height of buildings on both sides of the street. Vegetation and water features were added to the street as
visual components, while synthesized car pass-by sounds, water sounds, and bird songs were introduced assound stimuli. A laboratory experiment was conducted in a virtual reality (VR) environment with normal
hearing participants. The experiments consisted of three sessions: 1) an audio-only condition, 2) a combined
audio-visual condition, and 3) a visual-only condition. Participants were asked to rate their subjective
responses to stimuli in terms of the perceived pleasantness and perceived source width for the audio-only
condition, the perceived enclosure, perceived spaciousness, perceived pleasantness, and perceived source
width for the audio-visual combined condition, the perceived enclosure, perceived spaciousness, perceived
pleasantness for the visual-only condition. Additionally, the participant's physiological responses weremonitored during the experiment in terms of facial electromyography and heart rate.
Keywords: Enclosure; Street canyon; Height-to-Width ratio; Audio-visual interaction; Virtual Reality, Water
Sound, Birdsong.

1. INTRODUCTION
Enclosure specifies the street canyons, courtyards and other architectural spaces sur rounded bywalls or natural elements like trees [1]. Urban street canyons have been examined in terms ofenvironmental features like thermal comfort, natural light and the quality of air [2, 3, 4, 5]. Also,
street canyons have been investigated regarding the acoustical features (i.e., absorption and diffusionof the street boundaries).
Environmental and design aspects of the urban environments affect the perception of humans. The
findings of a previous study revealed that the sound environments of street ca nyons with different
height-to-width ratios significantly affect the perceptions of enclosures [6]. According to several
studies, natural sound sources like water and bird songs could have a positive effect on the perceptionof urban environments in terms of soundscape [7, 8]. Also, using water features improves the qualityof the acoustic environment with their feature of noise masking ; thus, it has been widely used to
improve the quality of urban soundscapes [7, 9].
Virtual reality (VR) technology for simulations offers a high degree of perceptual realism in theevaluation of the perception of the enclosure in different settings [10]. Furthermore, physiologicalmeasurements such as facial electromyography (EMG) are useful to understand people’s reactions todifferent stimuli in urban environments so these have been introduced in the acoustic research areas[11, 12, 13].
Hence, in this study, the influence of natural sound (i.e. water sound and birdsong) on theperception of the enclosure in urban street canyons with varying height -to-width ratios (H/W) areinvestigated. This study could provide information on issues conce rning audio-visual interactions,particularly for enclosures in urban streets with different designs. A laboratory experiment was
conducted in a virtual reality (VR) environment with normal hearing participants. Participants wereasked to rate their subjective responses to stimuli in terms of the perception of enclosure, spaciousness,pleasantness and source width. Additionally, the participant's physiological responses were monitored
during the experiment in terms of facial electromyography and heart rate.

2. METHODOLOGY
2.1 Participants
The participants were recruited after receiving ethical approval from the Central Ethics Committeeof the University of Liverpool (approval reference: 11249). Although our target number of participants
is 40 participants, 10 participants (5 males and 5 females) aged between 26 and 38 (M = 30.1, SD =3.8) have participated in the experiment. The target number of participants was chosen to achieve astatistical power of 80% (α < 0.05, two-tailed). None of the participants reported any hearing or visualdisabilities.
2.2 Street Canyons
The model employed a 500-m length of the street with continuous buildings along the sides atconstant heights. Street canyons of 27 m in width were chosen to represent wide urban streets. The
heights of the buildings were changed at three steps (13.5 m, 54 m, and 108 m); consequently, theH/W was 0.5, 2 and 4.
2.3 Stimuli
The visual stimuli for the visual-only sessions consisted of 21 three-dimensional (3D) street
canyons with different H/W levels and scenarios. The 3D models of the streets and buildings werecreated using 3DsMax, while natural features were added using Adobe Photoshop. For all H/W ratios,
natural features were added to the default setting which is the street canyons only with traffic. They
were 1) waterfalls, 2) fountains, 3) trees, 4) trees and waterfalls, 5) trees and fountains, and 6) flowers.
The audio-visual sessions consisted of 30 3D street canyon models with different H/W levels andscenarios. In addition to the scenarios used in the visual-only session, three more scenarios including
birdsong were used: 1) trees and birdsong, 2) trees, waterfall, and birdsong, and 3) trees, fountain,
and birdsong. The 3D models of the buildings were made using Sketchup and then were imported to
Unity to create the VR models. Additional visual edits to the roads and lighting were made in Unity
to make the virtual reality (VR) models more realistic. Moving vehicles were generated by using Unity
and Oculus Rift. The vehicles were then combined with the convolved sound sources. Thirty cars wereused in the VR models for all streets. Examples of the 3D models used in this study can be seen in
Figure 1.
The audio stimuli for the audio-only sessions consisted of 18 sounds. Road traffic noise was set asa reference and different sounds were then added according to the scenarios. Added sounds are 1)
waterfall, 2) fountain, 3) birdsong, 4) waterfall and birdsong, and 5) fountain and birdsong. For theroad traffic noise, a synthesised car pass-by sound [14] at a driving speed of 50 km/h was convolved
with the impulse responses from the computer simulation (ODEON). First, a 30-second-long car passby sound was cut into 301 pieces (0.09 second/piece) [6]. The impulse responses were generated fromthe acoustic simulations from ODEON software with one fixed receiver and 301 sound sources along
the street. A total of 602 impulses responses ((301 sources × two positions) × one receiver)) wereextracted for each street. Next, the 301 segments of the car pass-by sound were convolved with
impulse responses. The Hamming window of two-block length was implemented to generate thesmoothest and most realistic convolved car pass-by sound. For the scenarios with birdsong, bird soundwas added to the trees in the VR models [15]. The birdsong used in this study was the song of Robin.
For the scenarios with water features, water sounds recorded in an anechoic chamber were used [16].
The sound pressure levels of the road traffic noise were fixed at 60 dB at the lowest H/W level (i.e.
H/W = 0.5).
2.4 Procedure and design
Similar to some previous studies on audio-visual interactions [11,17], this experiment was designed
to include different sessions with and without the presence of sound stimuli. The expe riment consistedof three sessions: 1) Session 1: a visual-only condition; 2) Session 2: a combined audio-visualcondition and 3) Session 3: an audio-only condition.

Figure 1 – Streets with a building height of 13.5 m for only traffic, traffic with trees, traffic with waterfall
and trees, traffic with trees and fountain, traffic with flowers, and traffic with waterfall.
In the experiment, it was hypothesised that adding natural sounds like water sounds and bird songs
might have an impact on the perception of the enclosure. Another hypothesis was that H/W might
affect the perception of the enclosure in urban street canyons with natural features. In the audio-visualsessions, participants rated their perception of the enclosure, perception of spaciousn ess, perceptionof pleasantness and perception of source width. In visual-only sessions, participants rated theirperception of the enclosure, perception of spaciousness and perception of pleasantness. In the audioonly sessions, participants rated their perception of pleasantness and perception of source width.
The participants rated perceived enclosure on an 11-point numerical scale ranging from 0(extremely closed) to 10 (extremely open). The participants also rated the stimuli on an 11 -point
numerical scale of perceived spaciousness from 0 (not spacious at all) to 10 (extremely spacious),
whereas the perceived pleasantness was assessed on an 11-point numerical scale from 0 (not pleasantat all) to 10 (extremely pleasant). For the rating of the perceived source width, an 11-point numericalscale (with 0 representing ‘not wide at all’ and 10 representing ‘extremely wide’), which waspreviously used in a previous study [6], was adopted.
Two physiological responses including two facial electromyography data ( fEMG) and heart rate(HR) were recorded during the experiment. The Biopac MP150 physiological data acquisition system
and AcqKnowledge 4.4 (BIOPAC Systems) were used to acquire physiological signals. The fEMG
signals were measured using five electrodes placed over facial muscles, associated with two different
emotion expressions which are zygomatic muscle (to measure positive emotions) and corrugatorsupercilia (to measure negative emotions). The participant’s skin was gently rubbed with a skinpreparation gel in the areas where the sensors were placed to improve the quality of the fEMG signals.
Before starting the experiment, the impedance level of the skin was examined to confirm that it was
low enough for the signals collection (<5 kΩ) [18]. A photoplethysmography (PPG) sensor was
attached to the right index finger for measuring heart rate (HR). The participants were asked to avoid
large body movements. The experimental setup and a participant who is ready to start the experimentcan be seen in Figure 2.
The experiment is taking place in a listening booth with low background noise at the Acoustics
Research Unit, University of Liverpool. The participants were provided with a consent form andinformation sheet upon their arrival, they participated in the test after giving their consent. Eachparticipant was exposed to a total of 69 stimuli, including 21 visual stimuli, 30 combinations ofsimultaneous sound and visual stimuli, and 18 sound stimuli. In each session, there was 10 seconds
baseline before the stimuli start, after baseline, there was the sound and/or visual stimuli were
presented for 20 seconds, and the participants were given 15 seconds to rate the questions on a head mounted display (HMD, Oculus Rift). In each session, stimuli were randomly presented to avoid theorder effects. Before the real experiment started, participants attended a training session for aroundthree minutes to familiarise themselves with the virtual interface on the HMD and controllers. Training
sessions included one visual-only scene, one audio-visual scene, and one audio-only scene.

Figure 2 – Experimental setup and a participant before starting the experiment

3. RESULTS
Even though this study investigated several perceptions, only the perception of enclosure for thevisual only and audio-visual sessions will be presented in this section as a preliminary result. As it isan ongoing study, current results are from just 10 participants. After the completion of data collection

with 40 participants, the statistical analysis for all the subjective variables also physiological data will
be further analysed.
3.1

Effects of H/W and different Scenarios on perception of enclosure

Figure 3 shows the mean ratings of perceived enclosure across scenarios as a function of H/W: a)visual-only session and b) audio-visual combined session. It can be seen that ratings for the perceived
enclosure in visual-only sessions decreased with the increase in H/W for all scenarios with and without
natural features. Although perceived enclosure decreased with the increase in H/W, the highest
perception of the enclosure was seen in the scenario with traffic and flowers (V2). On the other hand,
the lowest rating was seen in the scenario with traffic, trees and fountains (V1W2). In the audio-visualsession, the lowest perception of the enclosure was seen in scenario V2 (traffic and trees).
A two-way repeated measures ANOVA (analysis of variance) was conducted to investigate theeffects of H/W and natural features on the perception of enclosure. It was found that there is asignificant main effect of the H/W on the subjective ratings for both visual only session ([F(2, 18) =18.978, p<0.01)]) and audio-visual session ([F(2, 18) = 20.516, p<0.01)]. It was also revealed that theeffects of different scenarios (i.e. adding natural features) on the subjective ratings are significant invisual-only sessions ([F(6, 54) = 3.872, p < 0.05)]), whereas it was not significant in audio-visualsessions ([F(9, 81) = 1.250, p > 0.01)]).

Figure 3 – Mean ratings of perceived enclosure for different scenarios as a function of H/W: a) visual only
session and b) audio-visual combined session

3.2

Effects of different scenarios on perception of enclosure

Figure 4 shows the mean ratings of perception of enclosure in visual -only sessions at different
H/Ws across scenarios. The black bars indicate the ratings for the default setting without any naturalfeatures. It was found that the ratings changed according to the visual images. Two-way repeatedmeasures of ANOVA were used to estimate the significance of the differences in the perception of
enclosure across the different H/Ws and scenarios. The effects of different scenarios on the perceptionof the enclosure were statistically significant ([F(6, 54) = 3.872, p<0.05)]) and the impacts of H/Wwere also significant ([F(2, 18) = 18.978, p < 0.01)]). Also, the interaction between H/W and different
scenarios was statistically significant (p<0.01).
η2 was calculated as a mean variability score for the perception of enclosure to determine andquantify the distinctiveness of the subjective responses. A higher η2 indicated a strong difference
between the H/W levels and different scenarios, whereas a low η2 indicated reduced subjective
distinctiveness regarding the perceived enclosure. The differences in the perceived enclosure invisual-only sessions are more significant for the effect of H/W than the effect of changing scenarios(η2 for the effect of H/W: 0.67 and η2 for the effect of scenarios).
The paired Wilcoxon signed-rank test was then conducted to see if the ratings from the scenarioswith natural features are different from those from the traffic only (reference). The results showedthat the differences there were three significant differences at the lowest H/W and one difference at
H/W=2; however, there was no difference at H/W=4.

Figure 4 – Mean ratings of perceived enclosure across scenarios in visual-only session: a) H/W=0.5, b)
H/W=2, and c) H/W=4. Asterisks indicate significant differences obtained from the Wilcoxon test (*p<0.05
and **p<0.01).
Figure 5 shows the mean ratings of perception of enclosure in audio -visual sessions at different
H/Ws across scenarios. Compared to the visual-only session, the variations of the ratings across the
scenarios were relatively small. In addition, the ratings of the streets with natural features were notstatistically different from those of the reference (i.e. traffic only). Two-way repeated measures of
ANOVA revealed that the scenarios across the different H/W did not make a significant change in theratings ([F(9, 81) = 1.250, p>0.01)]) but the effect of H/W was significant ([F(2, 18) = 20.516, p <0.01)]). It was found that the interaction between H/W and scenarios was significant (p<0.01). Similar
to the visual-only session, the effect of H/W on the perception of enclosure was greater than the
scenarios in the audio-visual session (η2 for the effect of H/W: 0.69 and η2 for the effect of scenarios:
0.12).

Figure 5 – Mean ratings of perceived enclosure across scenarios in audio-visual session: a) H/W=0.5, b)
H/W=2, and c) H/W=4.

SUMMARY
The influence of natural sound (i.e. water sound and birdsong) on the perception of the enclosurein urban street canyons with varying height-to-width ratios (H/W) were investigated. A laboratoryexperiment was conducted in a virtual reality (VR) environment with normal hearing participants.
Participants were asked to rate their subjective responses to stimuli in terms of the perception of
enclosure, spaciousness, pleasantness and source width. Additionally, the participant's physiologicalresponses were monitored during the experiment in terms of facial electromyography and heart rate.
The height-to-width ratio (H/W) were 0.5, 2 and 4 for street widths 27 m. The preliminary results
showed that the impacts of H/W and different scenarios on the perception of the enclosure weresignificant for most cases in visual-only sessions; however, it was not the case in audio-visual sessions.
This project is still ongoing so further analyses will be conducted with 40 participants along withphysiological responses.

REFERENCES
1. Ewing, R., and Handy, S. (2009). "Measuring the unmeasurable: Urban design qualities related towalkability," Journal of Urban Design 14, 65-84.
2. Ali-Toudert, F., and Mayer, H. (2006). "Numerical study on the effects of aspect ratio and orientation ofan urban street canyon on outdoor thermal comfort in hot and dry climate," Building and Environment
41, 94-108.
3. Memon, R. A., Leung, D. Y., and Liu, C.-H. (2010). "Effects of building aspect ratio and wind speed on
air temperatures in urban-like street canyons," Building and Environment 45, 176-188.
4. Chatzidimitriou, A., and Yannas, S. (2017). "Street canyon design and improvement potential for urban
open spaces; the influence of canyon aspect ratio and orientation on microclimate and outdoor comfort,"
Sustainable Cities and Society 33, 85-101.
5. Mei, S.-J., Luo, Z., Zhao, F.-Y., and Wang, H.-Q. (2019). "Street canyon ventilation and airborne pollutant
dispersion: 2-D versus 3-D CFD simulations," Sustainable Cities and Society 50, 101700.
6. Yilmaz, N. G., Lee, P.-J., Imran, M. & Jeong, J.-H., 2021. Effects of sound environment on perceived
enclosure in urban street canyons. Madeira, Portugal, s.n.
7. Jeon, J. Y., Lee, P. J., You, J., and Kang, J. (2010). "Perceptual assessment of quality of urban soundscapeswith combined noise sources and water sounds," Journal of the Acoustical Society of America 127, 13571366.
8. Hong, J. Y., and Jeon, J. Y. (2013). "Designing sound and visual components for enhancement of urban
soundscapes," The Journal of the Acoustical Society of America 134, 2026- 2036.
9. J. Kang, Urban Sound Environment (Taylor and Francis, New York, 2007), pp. 98–99.
10. H.H. Bqlthoff, H.A.H.C. van Veen, Vision and action in virtual environments: modern psychophysics inspatial cognition research, in: M. Jenkin, L. Harris (Eds.), Vision and Attention, Springer, New York, 2001,
pp. 233–252.
11. Park, S. H., Lee, P. J., Jung, T., and Swenson, A. (2020). "Effects of the aural and visual experience on
psycho-physiological recovery in urban and rural environments," Applied Acoustics 169, 107486.
12. Frescura, A., and Lee, P. J. (2022). "Emotions and physiological responses elicited by neighbours sounds
in wooden residential buildings," Building and Environment 210, 108729.
13. L. G. Tassinary, J. T. Cacioppo and E. J. Vanman, “The skeleto-motor system: surface electromyography.”
In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd ed., pp.
267–302). New York: Cambridge University Press, 2007.
14. Forssén, J., Hoffmann, A., and Kropp, W. (2018). "Auralization model for the perceptual evaluation oftyre–road noise," Applied Acoustics 132, 232-240.
15. InspectorJ, 9 May 2018. Bird Whistling, Robin, Single, 13.wav. [Sound Recording] (freesound.org)
16. Galbrun, L. and Ali, T.T., 2013. Acoustical and perceptual assessment of water sounds and their use overroad traffic noise. The Journal of the Acoustical Society of America, 133(1), pp.227-237.
17. Jeon, J. Y., Lee, P. J., You, J., and Kang, J. (2012). "Acoustical characteristics of water sounds forsoundscape enhancement in urban open spaces," Journal of the Acoustical Society of America 131, 21012109.
18. Park, S.H., Lee, P.J., Jung, T. and Swenson, A., 2020. Effects of the aural and visual experience on psychophysiological recovery in urban and rural environments. Applied Acoustics, 169, p.107486.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0507

Determining preferable levels of natural sounds for enhancing
noisy urban environments
Tae-Hui Kim1; Joo-Young Hong2
1

Department of Architectural Engineering, Chungnam National University, 34134, Republic of Korea

2

Department of Architectural Engineering, Chungnam National University, 34134, Republic of Korea

ABSTRACTThis study aims to assess preferable nature sound levels in noisy urban environments using a virtual reality
head-mounted device (VR HMD) under laboratory conditions. In this study, road traffic was selected as therepresentative urban noise, while two types of nature sounds (birdsong and stream) were chosen as preferable
nature sounds. For the laboratory experiment, the audio-visual environment is locations in outdoor areas
exposed to traffic noise from an expressway were recorded using a spherical panoramic camera with an
ambisonic microphone. In the experiment, traffic noise stimuli were calibrated at 65 dBA. Participants wereasked to adjust the birdsong or stream sound to their preferred level according to the traffic noise levels. In
each condition, participants also rated the perceived loudness of traffic (PLN) and overall soundscape quality(OSQ). Throughout the experiment, prediction models for preferred nature sound levels corresponding to theambient traffic noise levels were proposed.
Keywords: Soundscape, Virtual Reality, Natural sound, Traffic noise

1. INTRODUCTION
Using pleasant natural sounds to mask urban traffic noise is one of the most frequently usedsoundscape design approaches (1). Previous studies (2,3) have looked into how adding natural sounds,
such as birdsong and water sounds, might improve the perceived quality of acoustic environments. It
has been found that one of the crucial acoustic factors for creating pleasing soundscapes is the soundintensity of natural sounds. Hong et al. (2) conducted a laboratory test to investigate the most preferrednatural sound levels responding to traffic noise levels. They found that similar or 3 dB lower naturalsound levels were preferred when natural sounds were augmented with urban noise at levels of 55 dB
or 75 dB. Meanwhile, Hong et al. (3) also carried out an in-situ test to explore the preferable naturalsound levels corresponding to the ambient traffic noise levels and they showed that the most preferrednatural sound levels were found to be 5–6 dB higher than the findings from a previous study (2).
This difference between in-situ and laboratory experimental conditions could be the cause of thepreferred SNR discrepancy. There were controlled audio-visual environments in the in-situ experiment,
and there were some uncontrolled and extraneous factors, such as the traffic flow and meteorologicalconditions. Since participants took part in the experiment on different days, road traffic, andmeteorological conditions slightly varied across the participants, which might affect the results .
Furthermore, in previous studies (2,3), regarding the evaluation method, the participants wereasked to select one single preferred level of natural sound at each experimental condition. Thesemethods are limited in reflecting a range of preferred natural sound levels that results in the same
soundscape quality.
Thus, it is necessary to investigate ranges of preferred natural sound levels resulting in the same
soundscape quality enhancement according to the traffic noise levels. The range of the preferrednatural sound levels can be investigated based on psychoacoustic methods for determining thedifference limen (4–9). In this context, this study aims to investigate the range of preferred levels of1

kimtaehui@g.cnu.ac.kr

2jyhong@cnu.ac.kr

two types of natural sounds (bird and stream) in a controlled laboratory condition using a 3D audiovisual virtual reality technique.

2. METHOD
2.1 Participants
A total of 3 participants (1 male and 2 females) were recruited for the experiment; 3 participants
were student voluntary participants. All participants were non-acoustic experts, and the ethnic groupof the participants was Korean.
2.2 Stimuli
Two natural sounds and traffic noise was used for the laboratory experiment. For traffic noise,
audio and visual environments were recorded at student hostels in the Nanyang Technological
University, Singapore by using a spherical panoramic camera (Garmin VIRB 360 Action Camera,
USA) and an ambisonic microphone (ZYLIA ZM-1 Microphone, Poland). The selected location was
flanked by an expressway (8 lanes) so that traffic noise was dominant. From the traffic noise recording,a 6-min traffic noise sample was extracted. Two natural sounds (1-min), a birdsong (sparrow) and awater sound (stream), which were used in a previous study (3), were used as positive maskers.
The sound pressure levels (SPL) of the stimuli are plotted as a function of 1/3 octave bands from63 Hz to 8 kHz in Figure 1. The A-weighted SPL of the traffic noise, birdsong, and stream sound was
65 dBA. While the stream sound was dominated by mid to high-frequency components between 400
Hz and 8 kHz, the birdsong is predominately made up of high-frequency components over 2.5 kHz.
In terms of temporal characteristics, the water sound had low temporal variability and was consistent,
but the birdsong had considerable temporal variability and was intermittent.

Figure 1 – Acoustical characteristics of the traffic, birdsong, and stream(A-weighted sound pressure level at 65dB) in 1/3 octave band spectra
2.3 Experimental design
We interconnected the 360-degree video to an HMD via post-processing. To produce headphonebased stereo sound, we converted the A-format FOA recording to B-format FOA using Reapersoftware and used Unity software to down-mixed to binaural tracks. To improve the sense of reality
in the audio environment, we implemented real-time subject-head-rotation direction adjustment usinga VR-mounted head tracker.
Participants assessed the perceived loudness of traffic noise (PLN) and overall soundscape quality

(OSQ). Subsequently, represented the same traffic noise with natural sounds (i.e. birdsong, streamsound), then asked to adjust the natural sound to their preferred level according to the traffic noiselevel. After adjustment, re-assessed the traffic noise. Lastly, participants assessed the preferred range.
Participants rated the upper (or lower) limen relative to this standard level (preferred level). The
difference limen (DL) was assessed relative to this standard level. The preferred level of natural soundwas set as standard. The participants were asked to adjust the comparison level until it was ‘different’
from a standard level according to OSQ. For instance, if the standard (preferred) level was 68 dBA,
participants increase the comparison level until the perceived soundscape quality was ‘different’ fromthe standard. If 72 dBA was ‘different’ from standard, according to OSQ, 71.5 dBA (halfway between“equal” and “different”) was assigned to the upper limen (7). On the other hand, if 63 dBA was‘different’ from standard, according to OSQ, 64.5 dBA was assigned to lower limen. In each test case,participants were given the following instructions in chronological order: (I) assess ed the traffic noise,(II) adjusted the natural sound, (III) assessed the combined sound, and (IV) assessed the natural sound.
2.4 Procedure
For each of the two natural sounds, the participants were instructed to provide subjective
assessment in four steps, as described in 2.3. In Session I, the participants assessed the perceived
loudness of the traffic noise and overall soundscape quality. Both were evaluated with an 11-point
rating in this session.
After, in Session II, a randomly chosen natural sound was presented to the participant. The
participant was instructed to adjust the sound levels of the natural sound to their most preferred levels
while considering the perceived loudness of the traffic noise and overall soundscape quality. Thissession has been repeated a total of 6 times (3 x 2 natural sounds). The preferred level was assignedby average. Subsequently, in Session III, the participants were instructed to re-assessed the perceived
loudness of the traffic noise and overall soundscape quality.
Lastly, in Session IV, the participants were instructed to adjust the sound level of natural sound,when their perceived the overall soundscape quality was ‘different’ according to OSQ. A trial started
with the presentation of the standard level (preferred level). The participant pressed the right “trigger”
key of a VR controller when they increased (or decreased) the level until it was ‘different’. And theleft “trigger” key when they check the standard level. After participant pressed the “touchpad” key ofa VR controller when they judged the level as being ‘different’. After the response, the next trial
started with the presentation of the standard. This trial was repeated a total of 8 times (4 upper limen+ 4 lower limen).

3. RESULTS
Figure 3 (a) and (b) show the mean value of perceived loudness of traffic noise at 65 dBA. The
mean perceived loudness of traffic noise was 7.00 (SD = 1.00). When combined the birdsong withtraffic noise, the mean perceived loudness of traffic noise was 4.00 (SD = 1.00). And combined thestream with traffic noise, the mean perceived loudness of traffic noise was 3.3 (SD=1.53). Figure 4
shows the mean value of overall soundscape quality. The mean overall soundscape quality of traffic
noise was 5.33 (SD=2.08). When combined with the birdsong and traffic noise, the mean overallsoundscape quality was 6.00 (SD=1.00), And combined with the stream and traffic noise, the mean
overall soundscape quality was 5.67 (SD=1.53).
Figure 5 shows the average preferred level and preferred range at traffic noise 65 dBA. The
difference between the preferred level of birdsong and traffic noise level was 3.11 dBA (SD=3.95dBA), upper limen was 9.69 dBA, and lower limen was -3.64 dBA. And the difference between thepreferred level of birdsong and traffic noise level was 3.00 dBA (SD=3.47 dBA), upper limen was
8.67 dBA, and lower limen was -2.08 dBA.

Figure 2 – Mean rating score of perceived loudness of traffic noise. Error bars indicate standard errors.

Figure 3 – Mean rating score of overall soundscape quality. Error bars indicate standard errors.

Figure 4 – Mean preferred range of SNRs. (Natural sound level – Traffic noise level, 65 dBA)

4. CONCLUSIONSIn this study, laboratory experiments were carried out to examine the effects of natural sound types,
and the Range of natural sound levels according to traffic noise level on soundscape attribute using a
VR HMD. Despite the limited number of participants, the results showed that augmenting pleasantnatural sounds could decrease the perceived loudness of the target noise. Furthermore, this study could
investigate the range of preferred natural sound levels through the psychophysics method. The results
showed birdsong has almost 3dBA larger than stream sound. These results could be useful in thedevelopment of an adaptive natural sound generation system that interactively responses to ambient
acoustic environments.

ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea
government (MSIT) [No. 2022R1C1C1008066]. We are grateful to Prof. Woon-Seng GAN, Dr. Bhan Lam,
Zhen-Ting ONG, and Kenneth OOI, (the Digital Signal Processing Lab, Nanyang Technological University,
Singapore) for providing the experimental stimuli.

REFERENCES
1. Kang J, Aletta F, Gjestland TT, Brown LA, Botteldooren D, Schulte-Fortkamp B, et al. Ten questions on thesoundscapes of the built environment. Build Environ. 2016;108:284–94.
2. Hong JY, Ong ZT, Lam B, Ooi K, Gan WS, Kang J, et al. Effects of adding natural sounds to urban noises on
the perceived loudness of noise and soundscape quality. Sci Total Environ [Internet]. 2020;711:134571.
3. Hong JY, Lam B, Ong ZT, Ooi K, Gan WS, Kang J, et al. A mixed-reality approach to soundscape assessmentof

outdoorurban

environments

augmented

with

naturalsounds.

Build

Environ[Internet].

2021;194(February):107688.
4. Hernandez S V., Lawless HT. A method of adjustment for preferred levels of capsaicin in liquid and solid foodsystems among panelists of two ethnic groups and comparison to hedonic scaling. Food Qual Prefer.
1998;10(1):41–9.
5. Einat Lapid, , Rolf Ulrich TR. On estimating the difference limen in duration discrimination tasks: A
comparison of the 2AFC and the reminder task. Percept Psychophys [Internet]. 2018;70(2):291–305.
6. Davies WJ. Difference Limen for Level of Music. 17th Int Congr Acoust Rome. 2001;(Dl):1–2.
7. Gelfand SA. Hearing: An introduction to psychological and physiological acoustics, fourth edition. Hearing:
An Introduction to Psychological and Physiological Acoustics, Fourth Edition. 2004. 1–501 p.
8. Cunningham DW, Wallraven C. Experimental design: From user studies to psychophysics. Experimental
Design: From User Studies to Psychophysics. 2011. 1–387 p.
9. Kingdom FAA, Prins N. Psychophysics: A Practical Introduction. First edit. Academic Press; 2010. 280 p.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0147

Effects of biofeedback-informed soundscapes on well-being andcognition
Rodrigo Gutierrez1; Holger Schultheis2
1

AIT, Austrian Institute of Technology, Austria
2

University of Bremen, Germany

ABSTRACT
Acoustic environments of office spaces influence workers’ well-being and performance. This influence wasinvestigated with ISO12913 guidelines for soundscape studies. Using a within-subject design, 29 participants,
18 to 54 years old (Mean = 33.62, SD = 10.86); 6 women and 23 men performed a Reading Span Task (RST)
under three sound conditions: silence (E1), office environment (E2), and office environment mixed withoscillating sounds of nature (E3). The oscillatory frequency was set to each participant´s optimal breathing
rate, i.e., Resonant Frequency Breathing Rate (RFBR). Perception of the sound conditions, RST scores,
biological signals related to arousal, i.e., heart rate variability, skin conductance and temperature, personality
traits and executive functions’ deficits were assessed. Related sample t-test showed that E3 was perceived as
more pleasant and calm than E2, and more vibrant and eventful than E1. Further, spearman’s correlations
showed a positive relation between the identification of sounds of nature, perception of E3 as calm, heart ratevariability, working memory capacity and lower processing error in RST. Similarly, there is a positive
correlation between the perception of E3 as vibrant and skin conductance levels. These findings suggest that
oscillating sounds of nature facilitate optimal breathing, noise masking and attention, supporting the designof healthier and smarter working conditions.
Keywords: Soundscape, Biofeedback, Well-being

1. INTRODUCTION
Soundscape, defined as the perception of sound (1), can enhance health in daily activities (2, 3).
Studies show lower stress levels associated with positive soundscapes, i.e., acoustic environments
assessed as calm in the perceived affective quality model (4), the emotional attributions given to soundstimuli, implemented in ISO 12913 for soundscape studies. On the other hand, a lively acoustic
environment might be required which can also be perceived as positive (5). In consequence, thevibrancy dimension has been explored in dynamic social settings, highlighting that currently only thecalmness dimension has been studied enough to be officially recommended by environmental
authorities in the European Union (6). Hence, the need to continue research with soundscape standards
as operational tools that support well-being in the design of dynamic and cognitive demanding spaces.
For example, cognitive performance decline due to background speech, noise masking techniques
and restorative effects of water sounds have been broadly explored during open -plan offices’ tasks
and in rehabilitation settings (7, 8, 9, 10). Personalized indoor acoustic spaces have been studied fromthe perspective of communication and privacy preferences (11). Adaptive audiovisual stimulichanging with heart rate have been examined in virtual reality experiments in order to lower stressthrough guided slow-paced breathing (12, 13). Room acoustics parameters for quality open officespaces’ design have been established in international standards and used in several studies (14, 15,16). Lastly, companies with empirical evidence provide commercial solutions, e.g., water sculptures
to mask nearby conversations and provide pleasant audiovisual stimuli, overhead speaker arrays that
adaptively cover loud surrounding speech, or pod-like furniture that shells from the near sound fieldand visual periphery to improve privacy (17, 18, 19). As the evidence builds, it remains of interest to
focus on current understanding of auditory cognition and human psychophysiological research (20)12

rodrigo.gutierrez@ait.at.ac
schulth@uni-bremen.dein order to design acoustic environments that further support cognitive performance and health.
This task was approached by looking beyond the known restorative effects of pleasant soundscapes.
Namely, the influence of acoustic environments’ perception in indoor spaces during stressful
conditions. In other words, examining attention, memory and stress management while cognitive
performance tasks and relaxation guidance are provided simultaneously. Moreover, this work
hypothesizes on further associations that soundscape descriptors may have with sounds of natureoscillating at human’s optimal breathing rate, i.e., a rate at which health and cognition are known to
be enhanced (21). How do we perceive and interact with such augmented acoustic office environments,
i.e., with optimal breathing guidance made of natural sounds, during attention demanding officeactivities?

2. METHODS
Participants
The studied group consisted of 29 participants, 18 to 54 years old ( Mean = 33.62, SD = 10.86); 6
women, 23 men; with self-reported healthy hearing. The sample contained participants familiar withthe listening room where the experiment took place and with experience with audio quality listening
experiments. On the other hand, naïve participants also took part in the study. Informed consent forms
in compliance with the General Data Protection Regulation (GDPR) were signed by all participants.
Conditions
Three sound conditions were presented to every participant in Latin squ are design order. The
conditions consisted of recorded acoustic environments of office spaces, i.e., the control condition
silence (E1), office space, e.g., phone calls, workers conversations, kitchen activity and officeequipment usage (E2), and office space with sounds of nature, i.e., bird tweeting, water stream andoscillating wind gusts (E3). E2 was recorded with the Zylia-Z1 3 rd order ambisonics microphone and
the Zylia Studio Pro software calibrated according to the sound source’s spatial location, i.e., alistening position was selected to be surrounded by different office activities. The recordings format
was 19 channels, .wav files with 48khz sampling rate and 24-bit resolution. These recordings wherethen edited and mixed with Reaper v.5984 x64. The Zylia Studio Pro plugin was used to downmix the
19 channels to a 5.1.2 Dolby Atmos compliant surround speaker system available in the listening room
(22). The Sparta AmbiDRC compressor plugin was used in Reaper to clean background low frequency
noise. E3 included sounds of nature selected from high resolution (44.1Khz, 16 -bit) sound effects
libraries and mixed to provide breathing guidance to participants. The bird tweets indicated the startof inhalation, a wind gust indicated the start of exhalation and the water stream played continuously
to support the masking of the office activity from E2 that was played simultaneously. The mix ofsounds of nature aimed to resemble a natural forest acoustic environment which was played throughthe open-back headphones Beyerdynamic DT-990 pro, as the third sound condition to be studied. Ten
different mixes of the sounds of nature corresponding to breathing cycles betwee n 7s and 14s were
made. They were played for participants according to their measured optimal breathing rate, i.e.,
Resonant Frequency Breathing Rate (RFBR).
Measurements of equivalent continuous sound pressure level A weighted (LAeq) and Sound
Exposure Level (SEL/L AE) were taken. LAeq 5min = 32.9dB for E1 accurately represents the silence
for the control condition. LAeq 20min = 25.7dB and LAE = 56.0dB for E3 give more context to thevariations in sound level during E3, i.e., moments of low and moderate off ice activity. Sound Level
was measured at standard ear height of a sitting participant (1m). Measurements of LAeq and SEL/L AE
were obtained using the Behringer ECM 800 omnidirectional measuring mic, connected to an RME
Fireface 800 audio interface using Room EQ Wizard, REW v. 5.19 with calibrated files, and comparedwith the measurements of a Peaktech 8005 sound level meter. Mics were facing upward s.
Assessments
Psychological Assessments
Personality was accounted for by means of a short 5-minute, self-report scale consisting of ten
items (23). This short version of the Big 5 personality scale gives an overall assessment of openness
to experience, conscientiousness, extraversion, agreeableness and neuroticism or emotional stability.
The capacity to self-regulate attention and emotions across long-term goals, i.e., Executive
Functioning, was assessed with the Barkley Deficits in Executive Functioning Scale (BDEFS) (24).

This instrument has 20 items that can be finished within 5 minute s. BDEEFS scores and symptoms
count, i.e., number of highly rated items, are used to place the participant within a percentile range ofthe normative population provided by the BDEFS manual.
The subjective perception of the acoustic environment was measured in every sound condition Ewith Method A of ISO 12913 (25). It consists of four 5-point Likert scales containing 13 items, took
about 5 minutes to fill out and is useful to compare with similar soundscape studies. The first scale isfor sound source identification (Sid). It asked participants to what extent they heard sounds of officeequipment, human activity or sounds of nature. The second scale provides assessment for thePerceived affective quality (Paq) of 4 dimensions, i.e., pleasantness, calmness, vibrancy andeventfulness which are separated in eight descriptors in Method A. The third scale, Sound assessment(Sass), is an overall valence measure of good or bad. The fourth scale is Appropriateness (App), i.e.,
how fitting was the sound condition for an office space.
The effects on short span cognitive processes or lower-level Executive Functions (EF) were also
estimated for the three sound stimuli E with a Reading Span Task (RST) (26). The test provides theReaction Times, error rates and recalled-word scores of participants judging sentences they had toread out loud and words they had to remember presented after each sentence. Therefo re, RST measuresindicators of Working Memory Capacity (WMC) and is also a predictor of fluid intelligence (20). The
RST took approximately 15 minutes to complete and largely depended on the participant’s abilities.
This task requires an homogeneous population, because the spoken length of the words -to-beremembered affects the amount of words that can be stored in working memory, i.e., word -length
effect (20). An online version which could be done in English or German was used (26). A file withthe participant’s scores was generated at the end of the task.
Physiological Assessment
The physiological indicators analyzed in this study were Heart Rate Variab ility (HRV), Skin
Conductance Level (SCL) and Temperature (Temp). The system Somatic Vision Alive, Clinical
Version 3.2 (27), was used to acquire the physiological measurements. The administ ration of thisprocedure is non-invasive. Participants had to wear sensors on their non-dominant hand during theentire experiment. The sensors were attached to the participant’s fingers which had to be placed
comfortably to avoid movement and noise in the signal.
Procedure
The entire experiment took place in a listening room resembling an office working space with asurround speaker system to reproduce the sound conditions and was monitored and logged with aprotocol sheet. The main elements of the study setup are shown in Figure 1.
The BDEFS short self-report for normative EF and a brief version of the Big-Five personality
dimensions were administered once at the beginning of the experiment.
Next, a visual biofeedback, breathing protocol was administered. This slow-paced breathing
session consisted of 2 minutes inhaling and exhaling cycles at rates ranging between 7.5s and 14s oflength. The entire session lasted ca. 20 minutes. At the end, participants were asked if they werefeeling the same, more relaxed or uneasy compared to the beginning of the session. The Somatic
Vision Alive software calculated at which breathing rate the highest HRV average was produced, i.e.,
RFBR. This optimal breathing rate was used as the oscillating frequency of the auditory stimuli
delivered in condition E3 through open-back headphones. Besides the calculation of RFBR, thebreathing session provided a base-line relaxation state for every participant before starting the RST.
Afterwards, the within-subject experimental design presented every participant with each soundcondition E1, E2 and E3. These sound conditions were exposed in Latin square design order, i.e., E1,
E2, E3 for participant 1; E2, E3, E1 for participant 2; E3, E1, E2 for partici pant 3 and then repeatedfor subsequent participants. RST, biological signals and sound perception were measured for eachsound condition, i.e., a full factorial design. Before starting the RST for each sound condition,
participants were encouraged to allow themselves to continue breathing slowly and maintainrelaxation. They were also indicated that the RST was not a priority and were also instructed to balance
their attention between the RST task and their own breathing rate in order to maintain relaxatio n.
HRV, SCL and Temp were recorded during the RST in every sound condition and the recordings
were labelled with starting and ending times corresponding to the RST beginning and end.
After finishing each RST and while still under a sound condition, the pa rticipants were presented
with the ISO 12913-2 Method A scales. They were also asked: “Did you pace your breathing?”,including asking about the use of the sounds of nature as a breathing guide for E3. Keeping theheadphones on or taking them off was noted in E3.

Figure 1 – Experiment setup. Participants were exposed to sound conditions while performing the
RST and getting bio signals measured. Sound perception rating scales were administered after every
sound condition.

3. RESULTS
Subjective Sound Perception Scores
The rating scales of ISO 12913-2 Method A were used to evaluate the soundscape of participants.
The results are analyzed to answer RQ1: “What are the effects of an augmented acoustic officeenvironment, i.e., with breathing guidance made of natural sounds, on the different dimensions of ourperceived affective quality?”. Accordingly, the soundscape measurements of E3 were compared withthose of E2 and E1. Perceived affective quality (Paq), Sound source identification (Ssid), Soundassessment (Sass) and Appropriateness (App) were compared for the analysis of RQ1.
Paq was evaluated with a Likert scale composed of eight items, Paq1-Paq8 (pleasant, annoying,
eventful, uneventful, calm, chaotic, vibrant and monotonous). Each descriptor was rated by all theparticipants of this study during every sound condition E1, E2 and E3, with a 5-point Likert item from1 being “Fully agree” to 5 being “Fully disagree”. Figure 2 presents the average ratings of each
descriptor in a circumplex model. The model shows the relations between the eight descriptors andacross four Paq dimensions, i.e., Pleasantness, Eventfulness, Vibrancy and Calmness.

Perceived Affective Quality Means. E1, E2
and E3
Eventful
4
3
2
1
0

Chaotic

Annoying

Monotonous

Vibrant

Pleasant

Calm
Uneventful
E1

E2

E3

Figure 2 – Means of ratings for each descriptor of the Perceived affective quality (Paq ) scale.
Values were inverted to explicitly show positive trends, i.e., 5=Fully agree. Differences between E1,E2 and E3 can be observed by the distance between the data points markers. Additionally, overall
shapes indicate corresponding perceptual tendencies for each dimension axis, i.e., Pleasantness,

Eventfulness, Vibrancy and Calmness.
Frequency distribution analysis of the ratings given to the pleasant-annoying descriptors pairunderlined a pleasantness perception trend of the augmented sound condition E3. On the contrary,
more participants rated E2, i.e., the office acoustic environment (without sounds of nature), as lesspleasant and more annoying. Related-Samples Sign Test and Wilcoxon Signed Rank Test, executed in
SPSS statistical analysis software package, resulted in p < .001 and p = .001 correspondingly. Thus,
rejecting the null hypothesis H 0, i.e., the median of differences between E2Paq1 and E3Paq1 equals 0.
E1Paq1 and E1Paq6 exhibit the highest number of more pleasant and less annoying ratings . E1Paq1
notably differs from the pleasant perception distribution of E2 and, to a lesser extent, from E 3.
Executive Functioning Scores
The percentile scores of the Barkley Deficits in Executive Functioning Scales-Short Form, BDEFSSF, place each participant in their corresponding age group relative to a normative United States
population sample of 623 males of ages 18-93 years (49.9%, Mean age = 49.7 years, Standard
Deviation = 18.0) and 626 females of ages 18-96 years (Mean age = 49.8 years, Standard Deviation =18.0). BDEFS supports the finding of potential deficits in daily life goal -oriented activities in social
contexts such as the ones performed in occupational and educational roles, i.e., Self -Management toTime, Self-Organization, Self-Restraint, Self-Motivation and Self-Regulation of Emotions. BDEFS
scores were used to investigate potential correlations as explained next.
Observations of potential linear associations between BDEFS, ISO 12913 Method A andphysiological measures were carried out. Accordingly, the results of the BDEFS total scores andsymptoms count were used to further inspect associations. A Spearman's rank -order indicating thestrength and direction of the relationships between ordinal and/or continuous variables was conducted.
Given that associations were expected, a 1-tailed test was performed on all 29 participants BDEFS
and ISO 12913 Method A scores. Results of the correlation matrix showed a statistically significant
positive correlation between BDEFS total scores, and the identification of office sounds during E3(E3Sid1), rs = .347, p = .033. Similarly, a notable positive correlation was found between BDEFS
symptoms count and the perception of E3 as not calm (E3Paq5), rs = .351, p = .031. The identificationof natural sounds (E3Sid3) indicated a low negative association with BDEFS total scores, rs = -.289,
p = .064, that may be worth exploring further.
Furthermore, Bayesian correlation tests between BDEFS and physiological measures of 27
participants (inconsistent data from 2 participants was removed) provided insights additional to
Spearman’s rho (rs) with Bayes Factors (BF), i.e., the ratio of the likelihood of the null and alternative
hypotheses. The resulted low negative associations between BDEFS, heart rate variability (HRV) andtemperature (Temp) may be worthwhile exploring further given rs = -.222, BF 10 = 3.643 for HRV vs.
BDEFS total scores and rs = -.227, BF 10 = 3.536 for HRV vs. BDEFS symptoms count; and rs = -.230,
BF10 = 3.471 for HRV (correction it’s Temp) vs. BDEFS total scores and rs = -.304, BF 10 = 2.065 for
HRV (Temp)vs. BDEFS symptoms count.
Observed association tests between BDEFS and RST scores confirmed the broad evidence showingthat this two instruments measure different aspects of executive functions. Therefore, no meaningful
associations were found between them.
Cognitive Performance Scores
An A Reading Span Task (RST) was administered to participants in order to answer RQ2: “Howdoes the acoustic environment E3 and corresponding soundscapes correlate with the executive
cognitive abilities associated with attention?”. Cognitive performance measures of the RST include
Partial Credit Scores (PCS), Mean Reaction Times (MRT) and Mean Error Rates (Merr). PCS
corresponds to the average of the correct recalled words per block, i.e., Working Memory Capacity(WMC); MRT corresponds to the time average taken by participants to judge the sentences presented
in the RST as right or wrong; and Merr is the average of wrong a nswers to the judgements of thesentences, i.e., the processing task. A total of 22 participants did the RST in German and 7 did it inEnglish. Two of the participants who did the task in English had more than 9 errors (15%) in different
sound conditions. Hence, they were removed from the analysis.
A first observation of the RST measures indicated a small decreasing trend of PCS given by mean
± standard deviation of E1 = .81 ± .11, E2 = .80 ± .11 and E3 = .77 ± .15. Furthermore, classic andBayesian one-way repeated measures ANOVA tests were performed on the PCS of participants with

SPSS Statistics. There were no outliers in the data, as assessed by inspection of a boxplot. E1PCS andE2PCS scores were normally distributed, as assessed by Shapiro-Wilk's test (p = .178, p = .269
correspondingly). E3PCS was not normally distributed (p = .014), but one-way repeated measures
ANOVA can be considered robust to non-normality, and thus the results won’t be substantially affected.
Mauchly's test of sphericity resulted in not enough evidence indicating that the variances of thedifferences between all three combinations of EPCS are not equal, χ2 (2) = .56, p = .755. Consequently,
the required assumptions for the one-way repeated measures ANOVA tests were met. The test resulted
in no statistically significant differences in PCS means between the different sound conditions ( p= .159). Similarly, the Bayesian one-way repeated measures ANOVA test was used to find the ratiosof marginal likelihoods between alternative versus null hypotheses, i.e., the Bayes Factor, and thedifferences between posterior distributions of the three levels of the dependent continuous variable,i.e., E1PCS, E2PCS and E3PCS. The test resulted in a BF 10 = .178 indicating moderate evidence for
H0 , i.e., the data is 5.617 times more likely to be observed under the null hypothesis.
Given the results, each level of the within-subject variable PCS, i.e., E1PCS, E2PCS and E3PCS,
was broken down in groups by the order in which participants were exposed to the three soundconditions, i.e., E1E2E3, E2E3E1, E3E1E2. Figure 3 shows PCS ordered by the 1 st , 2 nd and 3 rd timethey were measured with their corresponding sound condition. This grouping exhibits the confounding
nature of stimuli exposure order, but more importantly, it facilitates the observation of performance
progression of different groups of participants, i.e., a between-subject perspective.

Partial Credit Score Averages Relative to order of Exposure
and Combinations of Sound Conditions
1.20
1.00
0.80
0.60
0.40

0.76

0.76

0.66

E1 1stE2 1stE3 1st

0.79

0.84

0.84

E1 2nd

E2 2nd

E3 2nd

0.90

0.81

0.81

E1 3rd

E2 3rd

E3 3rd

0.20
0.00
E1pcs

E2pcs

E3pcs

E1E2E3E2E3E1E3E1E2

Figure 3 – Partial Credit Scores arranged to compare the 1 st, 2nd and 3 rd order in which the soundconditions E1, E2 and E3 were presented. For example, the 1st time participants did the RST, the ones
exposed to E3 (green) got the lowest PCS, but this group (E3E1E2) got the same scores on the 3 rdtime they did the RST as the E2E3E1 group. The group of participants who did E2E3E1(squares)
achieved the highest PCS grand mean, 0.83. On the contrary, E3E1E2 (triangles) scored lowest, 0.75.
E1E2E3 (circles) averaged 0.80.
The previous analyses of variance are an approach to partially answer RQ2, i.e., Which associations
are there between E3 and cognitive abilities? The other part of RQ2 is: “Which associations are there
between E3’s elicited soundscapes and cognitive abilities?”. The latter requires a statistical test thatcan find the relations between ordinal and continuous variables. Spearman’s correlation provides suchcapabilities and was used to investigate the relations between the 13 subjective descriptors of ISO
12913-2 Method A and PCS, Merr and MRT. Similarly, testing H psy : “Soundscape dimensions of theperceived affective quality model, influenced by an optimal-breathing-rate guide of natural sounds,
improve cognitive performance” was included in the analysis of Spearman’s correlation tests. The
assumption of existing influences required a 1-tailed approach. Statistically significant associations
were found between higher ratings of E3Paq8 (perception of E3 as not monotonous), higher PCS(WMC) and lower Merr (processing) measures (p < 0.05 in both cases). The inverse relation between
E3Sid3 (identification of sounds of nature) and Merr, and the direct relation between E3Sid2(identification of sounds of humans) and MRT are also notable (also p < 0.05 in both cases).

Physiological Measurements
The analysis of measurements of 3 indicators related to the Autonomic Nervous System (ANS)
activity are shown in this section in order to answer RQ4: “Are the elicited soundscapes healthy, i.e.,do they lower stress levels?”. Moreover, the hypothesis H phy : “Soundscape dimensions of theperceived affective quality model, influenced by an optimal-breathing-rate guide made of naturalsounds, improve health” is also tested in this section. The captured data from sensors placed on thefingers provided average Heart Rate Variability (HRV), Skin Conductance Level (SCL) and
Temperature (Temp) for all 29 participants. The measures were taken during the RST for all soundconditions E1, E2 and E3. Two participants were removed from the analysis due to partial loss of datacaused by sensor movement during different sound conditions. HRV is an indicator of coherence, i.e.,
synchronization between the cardiovascular, respiratory and blood pressure systems. Therefore, it is
the main metric used in this study to establish a healthy state. SCL indicates sympathetic nervous
system activity, i.e., arousal and increase of sweat due to stressful events or cognitive workload.
Parasympathetic nervous system activity, i.e., relaxation, can be derived from the temperature of thefingertips caused by blood flow. As explained in previous sections, auditory biofeedback was used insound condition E3 to guide slow-paced breathing, and thus supporting the increase of HRV and Temp
and the decrease of SCL. Table 1 shows the results of the correlation tests, i.e., Spearman’s coefficientrs . Given the assumptions of Hphy, 1-tailed tests were conducted. Statistically significant associations(p values < 0.05) were found between higher ratings of E3Sid3 (identification of sounds of nature)and higher HRV measures (ANS coherence); higher ratings of E3Paq5 (perception of E3 as calm) andhigher HRV levels; and higher ratings of E3Paq3 (perception of E3 as vibrant) and higher SCL levels(arousal). Several other associations are worth investigating further, e.g., E3Paq6, E3Sass and HRV;
E3Paq2 and SCL; and E3Paq7 and Temp.
Table 1 – Significant relationships between soundscapes and physiological measures. 27 cases werecorrelated. Sid Likert items were coded 1 for “Not at all” to 5 for “Dominates completely”; Paq items werecoded 1 for “Fully agree” to 5 for “Fully disagree”; Sass were 1 for “Very good” to 5 for “Very bad”
E3Sid3

E3Paq2 E3Paq3 E3Paq5 E3Paq6 E3Paq7 E3Sass

.469

.195

.107

-.444

.354

-.024

-.362

Sig. (1-tailed)

.007

.165

.297

.010

.035

.453

.032

N

27

27

27

27

27

27

27

.167

-.345

-.408

.151

-.194

.022

.153

Sig. (1-tailed)

.202

.039

.017

.226

.165

.457

.224

N

27

27

27

27

27

27

27

-.021

.080

-.076

.059

.181

-.344

-.289

Sig. (1-tailed)

.458

.345

.354

.385

.184

.040

.072

N

27

27

27

27

27

27

27

E3HRV Correlation(%)

Coefficient

E3SCL Correlation(μS)

Coefficient

E3Temp Correlation(°C)

Coefficient

4. DISCUSSION
Posterior distributions of the Reading Span Task (RST) measures, i.e., Partial Credit Scores (PCS),
Mean errors (Merr) and Mean Reaction Times (MRT) reflect the difficulty in attention management
required during E3. Following a breathing guide while performing a cognitive task that includes
speaking, greatly affected the cognitive load of participants. This is a rather unusual situation in reallife and constitutes an important limitation to the main hypothesis of this study, i.e., H: “Soundscapedimensions of the perceived affective quality model, influenced by an optimal-breathing-rate guideof natural sounds, improve cognitive performance and health”. On the other hand, the data also showsthat participants exposed to E3 and E2 performed equally after get ting practice with the RST.
Therefore, answering RQ2: “How does the acoustic environment E3 and corresponding soundscapes
correlate with the executive cognitive abilities associated with attention?”, requires a more appropriate
method for the study. An adaptation of the RST that does not require speaking may be worthwhile
investigating. However, the fact that RST scores had no statistical difference between them in the
cognitive performance analyses of Section 3.3, does not mean there isn’t one. Given that participants
were instructed to slow-pace their breathing in each sound condition (not only during E3), but deeper
levels of analysis could also discover underlying effects as discussed next.
Further analysis of covariance such as ordinal logistic regression or a between-subject study, could
elucidate why the participants performed differently depending on the order of sound conditions
exposure (see Figure 3). For instance, why did participants exposed to the sound conditions in theorder of E2E3E1 performed better than the others in the RST? Breaking down the participants ingroups by sound combination order was only done for further PCS analysis. Therefore, it is likely that
additional insights could surface by following the same procedure for the other me asures of RST andfor the other dependent variables.
Answering RQ2 revealed only weak associations between E3 and the RST administered in thisstudy. However, answering RQ2 also exposed strong associations between some subjective
perceptions of E3 and RST performance, e.g., higher identification of natural sounds was associatedwith lower errors in processing the validity of the sentences presented in the RST. This is an interesting
perspective that supports the soundscape approach, i.e., the importance of perception as a fundamental
criterion in the design of our acoustic environments.
The physiological assessment of Heart Rate Variability (HRV), Skin Conductance Level (SCL) and
Temperature (Temp) was another fundamental part of this project. It served the purpose to answer
RQ4: “Are the elicited soundscapes (of E3) healthy, i.e., do they lower stress levels?”. Measurementswith outliers included showed that E2HRV and E3HRV were almost identical and slightly higher than
E1HRV, i.e., in the ranges of 86% and 87%. This indicated that participants were slow pacing theirbreathing rates in each sound conditions as they were reminded to do so at the beginning of each RST.
Participants also responded affirmatively when asked: “Did you allow yourself to slow -pace yourbreathing?” after each RST. Given that speaking during the RST didn’t allow to properly maintain thebreathing rate, it is quite surprising to find such levels of HRV averages. Similarly, the temperature
grand average was around 29.13°C.
Furthermore, when the outliers were removed, the HRV scores evidenced a better performance
during E3. Again, why were there participants (outliers) with low HRV measurements between 62%
and 71% during different sound conditions? Conducting an analysis of covariance coul d clarify theunderlying reasons.
The physiological analysis also had a part in the test of the main hypothesis H: “Soundscapedimensions of the perceived affective quality model, influenced by an optimal -breathing-rate guideof natural sounds, improve cognitive performance and health”. Therefore, the associations found in
Section 3.2 between some soundscapes and physiological measures, e.g., higher identification ofnatural sounds were associated with higher levels of HRV, support the concluding arguments discussed
in the next chapter.
Finally, the Barkley Deficits in Executive Functions (BDEFS) and the Ten Item Personality
Inventory (TIPI) scores also played a role in the interactions between variables. Highly perceiving E3
as calm (E3Paq5) correlates with higher HRV, lower BDEFS symptom count and higher ratings ofopenness. Other variables are shared between two disciplines, but to this point E3Paq5 is the only one
relating executive functioning management, physiological coherence, a curious and creative
personality trait and the subjective perception of E3 as calm. It is necessary to keep in mind that the
BDEFS normative population is from the US and thus, the scores might vary if they are related to adifferent normative population.
Additionally, it is important to also add the role of acoustics in this research. ISO12913 emphasizes
the perceptual dimension, but it also requires acoustic characterizations and reporting of associations.
Nonetheless, the sound condition E3 added auditory stimuli that were re produced on headphones.
Measuring such acoustic characteristics requires a head measurement system which was not available.
Hence, E3 was measured using the same speaker system as E2. The obtained acoustic measurements,
recorded media, software and hardware settings were also backed up to provide support for further

analysis or verification if required.
To finish on an open note, the following questioning is presented: Are the sound conditions having
an effect beyond the responses elicited by the Likert questionnaires? Is E3 disruptive? How did priorknowledge of E1, E2 or E3 affect posterior reaction to stimuli? According to modern Bayesian theories
explaining the neuroscience of cognition, perception and action, humans do not only react to external
early levels of sensory stimuli to create more sophisticated perceptions as these signals travel into
deeper areas of the brain, i.e., as in a bottom-up, outside-in or feedforward control system. Perception
is also shaped by top-down, inside-out, feedback processing that conveys a prediction of sensory
signals. In other words, participants learned from the corresponding sound condition they were
exposed to first. They performed better the 2 nd time and differently 3 rd time. How do we minimize theprediction error, i.e., achieve a sustained relaxed focused attention state mirroring the interoceptive
inference of the brain? How is cognitive performance influenced by physiological reaction awareness?

5. CONCLUSIONS
People working in open office spaces where human activity negatively affects their cognition andincreases their stress, can benefit from sounds of nature oscillating at their individual optimal
breathing rate. Such biofeedback-informed acoustic environment provides a slow-paced breathingguide that may result in a calmer perception of the acoustic environment that supports cognitive
performance and health. The influence of this type of oscillating natural sound is also associated withemotional management and some personality traits.
No prior evidence of the associations between the Barkley Deficits in Executive Functioning Scales(BDEFS) and soundscape descriptors was found during this research. Therefore, it is important tocontinue investigating this variable as a potential predictor of soundscape. A study wit h the longer
versions and other-report scales of the BDEFS should be employed. Similarly, given the evidence ofthe correlation between personality and soundscape, it is also advisable to implement more complete
personality questionnaires.
Correlations between the characterization of the acoustic environment, psychoacoustic parametersand soundscapes are needed in order to have a more complete picture of their interaction withmeasures of emotional management, personality, cognition and health.
Speculations derived from the analysis of Section 4, point in the direction of future work on
adaptive acoustic environments according to individual preferences and biological signals.
Facilitating the identification of sounds of nature should be prioritiz ed in such scenarios. Moreevidence is needed to strengthen the underlying factors that may cause variations in cognitive
performance and health measurements.

ACKNOWLEDGEMENTS
I would like to acknowledge the assistance of Marcel Neberich, who bridged the collaboration
between Dolby Germany GmbH and the Technische Hochschule Nuremberg, providing key resources
and insights in architectural psychology. I would also like to acknowledge the support of the personnel
at Dolby, whose expertise in state-of-the-art product development was of great value.

REFERENCES
1. ISO - ISO 12913-1:2014 - Acoustics — Soundscape — Part 1: Definition and conceptual framework.
[Online]. Available from: https://www.iso.org/standard/52161.html.
2. Aletta F, Oberman T, Kang J. Associations between Positive Health-Related Effects and Soundscapes
Perceptual Constructs: A Systematic Review. International Journal of Environmental Research andPublic Health; 29 October 2018; 15(11): 2392.
3. Medvedev O, Shepherd D, Hautus MJ. The restorative potential of soundscapes: A physiologicalinvestigation. Applied Acoustics. 1 September 2015 1; 96: 20-26.
4. Axelsson Ö, Nilsson ME, Berglund B. A principal components model of soundscape perception. The
Journal of the Acoustical Society of America. November 2010; 128(5): 2836-2846.
5. Handbook of Research on Perception-Driven Approaches to Urban Assessment and Design:
9781522536376: Science & Engineering Books | IGI Global. [Online]. Available from:https://www.igi-global.com/book/handbook-research-perception-driven-approaches/181221. p. 153.

6. Aletta F, Kang J. Towards an Urban Vibrancy Model: A Soundscape Approach. International Journal ofEnvironmental Research and Public Health. 10 August 2018; 15(8): 1712.
7. Jahncke H, Hongisto V, Virjonen P. Cognitive performance during irrelevant speech: Effects of speech
intelligibility and office-task characteristics. Applied Acoustics. March 2013; 74(3): 307-316.
8. Jahncke H, Hygge S, Halin N, Green AM, Dimberg K. Open-plan office noise: Cognitive performanceand restoration. Journal of Environmental Psychology. December 2011; 31(4): 373-382.
9. Hongisto VO. Effect of sound masking on workers in an open office. In Proceedings - European
Conference on Noise Control; 2008: Acoustical Society of America. p. 537-542.
10. Cerwén G, Pedersen E, Pálsdóttir AM. The Role of Soundscape in Nature-Based Rehabilitation: A
Patient Perspective. International Journal of Environmental Research and Public Health. 11
December 2016; 13(12): 1229.
11. Lundgaard SS, Nielsen PA. Personalised soundscapes in homes. In DIS 2019 - Proceedings of the 2019
ACM Designing Interactive Systems Conference; 2019: Association for Computing Machinery, Inc.
p. 813-822.
12. Blum J, Rockstroh C, Göritz AS. Heart Rate Variability Biofeedback Based on Slow-Paced Breathing
With Immersive Virtual Reality Nature Scenery. Frontiers in Psychology. 20 September 2019;
10(SEP): 2172.
13. Yu B, Funk M, Hu J, Wang Q, Feijs L. Biofeedback for Everyday Stress Management: A Systematic
Review. Frontiers in ICT. 7 September 2018; 5(SEP): 23.
14. Rindel JH, Christensen CL. Acoustical simulation of open-plan offices according to ISO 3382-3.
15. Lee Y, Aletta F. Acoustical planning for workplace health and well-being: A case study in four openplan offices. Building Acoustics. 16 September 2019; 26(3): 207-220.
16. Selzer J, Schelle F. Practical aspects of measuring acoustics in German open plan offices. ; 2018.
17. Cambridge Sound Management - The Sound Masking Leader. [Online]. Available from:https://cambridgesound.com/.
18. Plantronics Unveils Habitat Soundscaping; An Intelligent Service to Solve Noise and Distraction
Problems Associated With Open Office Environments | Poly Online Newsroom. [Online]. Available
from:https://newsroom.poly.com/press-release/contact-center/plantronics-unveils-habitatsoundscaping-intelligent-service-solve-nois.
19. Steelcase - Office Furniture Solutions, Education & Healthcare Furniture. [Online]. Available from:https://www.steelcase.com/eu-en/#our-research.
20. BALDWIN CL. AUDITORY COGNITION AND HUMAN PERFORMANCE : research andapplications.: CRC PRESS; 2019.
21. Khazan IZ. The clinical handbook of biofeedback : a step by step guide for training and practice withmindfulness.
22. Dolby Atmos ® Home Theater Installation Guidelines. ; 2018.
23. Gosling SD, Rentfrow PJ, Swann WB. A very brief measure of the Big-Five personality domains.
Journal of Research in Personality. 2003; 37(6): 504-528.
24. Barkley RA. Barkley Deficits in Executive Functioning Scale (BDEFS): Guilford Press, New York, NY;
2011.
25. ISO - ISO/TS 12913-2:2018 - Acoustics — Soundscape — Part 2: Data collection and reporting
requirements. [Online]. Available from: https://www.iso.org/standard/75267.html.
26. Klaus J, Schriefers H. Measuring verbal working memory capacity: A reading span task for laboratory
and web-based use. .
27. Alive
ClinicalVersion|somaticvision.com.
[Online].
Availablefrom:https://www.somaticvision.com/products/aliveclinical.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0373

Subjective salience for birdsongs and insect songs in noise
environments
Yoshiharu SOETA; Ayaka ARIKI1
National Institute of Advanced Industrial Science and Technology (AIST), Osaka, Japan

ABSTRACT
Birdsong is used to communicate the position of stairwells to visually impaired people in train stationsin Japan. However, more than 40% of visually impaired people reported that such sounds were difficult
to identify. Train companies seek to present the sounds at a sound pressure level that is loud enoughto be detected, but not so loud as to be annoying. Therefore, salient birdsongs with relatively low
sound pressure levels are required. In the current study, we examined the salience of different types ofbirdsong and insect song in noise environments. We considered insect songs because both birdsongs
and insect songs have been found to have positive effects on soundscapes. We evaluated subjective
saliences for birdsongs and insect songs with equal sound pressure level using paired comparison
methods. 25 participants evaluated 6 types of birdsongs and insect songs in 3 noise conditions. The
relationships between subjective salience and physical parameters were examined. The results
indicated that Horornis diphone and Meimuna opalifera were the most salient types of birdsongs andinsect songs, respectively. The blunt birdsongs with lower pitch and sharper insect songs with lower
fluctuation strength were perceived to be more salient.
Keywords: Soundscape, Salience, Familiarity, Birdsong, Insect song

1. INTRODUCTION
Birdsong is almost universal experiences in the outdoor environment. Some birdsongs
communicate seasonal changes and are considered pleasant by many Japanese listeners (1). While not
all birdsongs are considered by humans to be beneficial components of an environment, some havebeen found to contribute to perceived attention restoration and stress recovery (2, 3). From theperspective of soundscapes, natural sounds (e.g., water, birdsongs, and wind in trees) can play a keyrole in acoustic comfort. Water sounds are often used to mask other sounds and as noise barriers toenhance urban soundscapes (4-6). The introduction of birdsong has been found to increase thesubjective pleasantness of soundscapes in public spaces (7). Among various natural sounds, birdsong
was judged as the most effective and beneficial type of sound for improving sound environments (810).
In Japanese public spaces, sound signals are often used to guide vi sually impaired people tospecific destinations, such as a ticket gate or staircase. For instance, birdsong is often used to signal
the presence of a staircase in train stations. However, more than 40% of visually impaired people
reported that a birdsong stimulus was difficult to localize in train stations (11). Although guidelines
exist regarding the use of birdsongs as information signals (12), these are not always strictly followed
by train company staff, who may prefer to use lower-than-recommended sound pressure levels (SPLs)
to reduce discomfort in customers, staff, and surrounding residents.
The physical factors that affect the sound signals used to guide visually impaired people have been
investigated from the viewpoint of sound localization. Based on the percentages of correct localization
responses, researchers have found that the signal-to-noise ratio, initial delay time, reverberationenergy, distance, elevation angle, and the temporal pattern of the signal all affect sound localization(13, 14). Additionally, researchers have proposed that sounds with specific temporal patterns might
be more easily detectable by visually impaired people and that these factors might be uncovered byy.soeta@aist.go.jp, ariki.yxuric@aist.go.jp

examining human brain responses (15). Saliences of birdsong and insect song in terms of several
physical parameters have been investigated (16). Horornis diphone and Cuculus canorus produce themost salient birdsongs, while Meimuna opalifera and Oncotympana maculaticollis produce the mostsalient insect songs. All of these creatures are well-known in Japan. The variation of loudness,roughness, and spectral skewness were significant predictors of salience for both birdsongs and insect
songs. However, birdsongs are presented in some kinds of noise in real situations. The effects of thenoises on the salience of birdsongs and insect songs have not been examined yet.
The aim of this study was to evaluate the salience of a number of birdsongs in noise environments,
and to determine the physical factors that modulated the observed salience. Here, we used the term‘salience’ to refer to whether or not a sound stood out from background noise. Although the abovementioned guidelines include an appropriate volume in SPL, they do not consider variations among
specific sound sources (12). To address this in the present study, we investigated the subjective
salience of birdsong presented with equal SPLs.

2. METHODS
2.1 Subjective test
We used 6 types of birdsongs and insect songs that had been used in previous experiments (1). The
birdsongs stimuli were produced by Cuculus canorus (CC), Cuculus saturatus (CS), Eophonapersonata (EP), Garrulus glandarius (GG), Horornis diphone (HD), and Zosterops japonicus (ZJ). The
temporal waveforms of the birdsongs are shown in Figure 1 and the spectrograms of the sounds areshown in Figure 2. The insect songs stimuli were produced by Meloimorpha japonica (MJ), Meimuna
opalifera (MO), Oecanthus longicauda (OL), Oncotympana maculaticollis (OM), Tanna japonensis(TJ), and Velarifictorus micado (VM). The temporal waveforms of the insect songs are shown in
Figure 3 and the spectrograms of the sounds are shown in Figure 4.
Three noisy background conditions—in a room, subway station, and car cabin—were simulated.
The first one was simulated by Hoth noise (17), which has the power spectrum of room noise. The
second one was simulated by a random noise that had the averaged power spectrum of actual
recordings in a subway station (18). The third one was simulated by an actual noise recorded in a carcabin. Each background noise was presented from 250 ms before the birdsong onset to the birdsong
offset. There were three background noise conditions for each birdsong and insect song condition,
resulting in a total of 18 birdsong and insect songs stimuli.
We tested subjective salience by presenting birdsong and insect song stimuli with durations
between 0.4 and 2.0 s. The stimuli were presented to participants using a headphone amplifier(HDVD800, Sennheiser, Germany) and headphones (HD800, Sennheiser, Germany). The participants
listened to the stimuli while sitting in a soundproof room with an ambient temperature of 22–25
degrees. The A-weighted sound pressure levels (SPLs), L Aeq , of the songs and background noises were
fixed at 60 dBA in all experiments. The stimuli in the L Aeq were verified using a dummy headmicrophone (KU100, Neumann, Germany) and a sound calibrator (Type 4231, Brüel & Kjær,Denmark).

Figure 1 – Temporal waveforms of the 6 birdsongs.

Figure 2 – Spectrograms of the 6 birdsongs.

Figure 3 – Temporal waveforms of the 6 insect songs.

Figure 4 – Spectrograms of the 6 insect songs.
25 participants aged between 20 and 41 years joined in subjective salience tests. All participants
had normal hearing and no history of neurological disease. Informed consent was obtained from eachparticipant after ex-plaining the nature of the study. The study was approved by the ethics committeeof the National Institute of Advanced Industrial Science and Technology (AIST) of Japan.
We used the modified Scheffe’s paired comparison method (19-21) to measure subjective salience.
In our protocol, we performed all pairwise comparisons for each iteration for each participant. All
combinations of birdsong and insect songs pairs (i.e., 153 pairs (N(N−1)/2, N = 18) were presented inrandom order, and the presentation order within each pair was also randomized. The silent interval
between the stimuli was 1.0 s long. Following the presentation of each pair, the participants wereasked to judge their salience using a seven-point scale. The averaged salience values were calculated
based on the modified Scheffe’s method and were defined as scale values (SVs) of salience. An
analysis of variance (ANOVA) was conducted on the results of the paired comparison experiments(19-21).
2.2 Physical parameters
To quantify the acoustic characteristics of the birdsongs and insect songs, we analyzed specificphysical parameters. First, we analyzed four parameters using autocorrelation function (ACF) analysis.
The first parameter is the delay time of the first maximum peak, 1 , which is related to the perceived
pitch. The second parameter is the amplitude of the first maximum peak, 1 , which is related to thestrength of the perceived pitch. The third parameter is the effective duration of the envelope of thenormalized ACF, e, which is defined by the ten-percentile delay and represents a repetitive component
in the sound source itself. The fourth parameter is the width of the first decay, W (0) , which is thecounterpart to the spectral centroid (22, 23).
Based on the results of the interaural cross-correlation function (IACF) analysis, we evaluatedthree parameters. The first factor was the interaural cross-correlation coefficient (IACC), which was
defined by the maximum value of the IACF. The IACC is related to spatial impression (24). The
second parameter was the interaural time delay, τIACC, which was defined by the delay time at the
IACC. The third parameter was the width of the IACF, W IACC, which was defined by the interval ofthe delay time at a value of 10% below the IACC (25).
We then analyzed four psychoacoustic parameters: loudness, sharpness, roughness, and fluctuation
strength (26). Loudness is the psychological strength of a sound. Sharpness relates to the balance ofthe high and low frequency components of a sound. Roughness and fluctuation strength quantify the
subjective perception of the rapid (15−300 Hz) and slow (at frequencies up to 20 Hz) amplitude
modulation of a sound.
2.3 Multiple regression analysis
To identify and quantify the physical factors that affect subjective salience, we carried out multiple
regression analyses with stepwise selection using a linear combination of the physical parameters andthe standard deviations (SDs). The stepping criteria used for entry and removal were based on thesignificance level of the F-value and were set at 0.05 and 0.10, respectively. Factors with a varianceinflation factor of 3.0 or more were excluded to avoid multicollinearity. The analyses were carried out
using SPSS statistical analysis software (SPSS version 22.0, IBM Corp., NY).

3. RESULTS AND DISCUSSION

Figure 5 – Scale value of salience for birdsongs in noise environments (H: Hoth, S: Station, C: Car).

Figure 6 – Scale value of salience for insect songs in noise environments (H: Hoth, S: Station, C: Car).
Figure 5 shows the SVs of salience for birdsongs in noises. The most salient birdsong was that of
HD. The relatively salient birdsongs were that of GG and CC. HD is one of three major species ofpasseriforme in Japan, which are known for their beautiful vocalizations. Its birdsong was the most
preferred stimulus in the preference test (1). GG was the most salient, HD and CC were the second
and third salient stimuli in silent condition (16). These results are consistent with the present fi ndings.
Since ancient times, CC has appeared in various documents in Japan, and its song is often compared
to onomatopoeia. Compared with other stimuli, it was found to elicit the largest brain responses, andthese responses were most strongly correlated to the sound envelope in the human brain (15).
Figure 6 shows the SVs of salience for insect songs in noises. The most salient insect song wasthat of MO, and the second and third salient insect songs were that of OM and TJ, which are all cicadas.
These results are consistent with the previous findings in silent condition. Cicadas are famous in Japanas noisy insects in the summertime. Noisiness might have effect on salience in noise.
We conducted a multiple linear regression analysis with the SVs of salience for birdsongs andinsect songs in noise environments as the outcome variable. The final model showed that sharpnessand 1 were significant parameters for birdsongs, while W (0), fluctuation strength, and sharpness_SD
were significant parameters for insect songs:
SVbirdsongs in noise environments  a0 + a1*sharpness + a2*1 ,(1)
SVinsect songs in noise environments  b0 + b 1*W(0) + b 2*fluctuation strength + b 3*sharpness_SD (2)The ANOVA indicated the statistical significance of the model (F[2, 15] = 18.81, p < 0.01, forbirdsongs, F[3, 14] = 36.66 p < 0.01, for insect songs). The adjusted coefficient of determination, R 2,
was 0.68 for birdsongs and 0.86 for insect songs. The standardized partial regression coefficients inEquations (1) and (2) are summarized in Table 1.
Table 1 – Significant predictive parameters and standardized partial regression coefficients revealed bymultiple linear regression analyses. Asterisks indicate the level of significance, i.e., **p < 0.01, *p < 0.05.
Significant predictive parameter
Birdsongs
Insect songs

a1, sharpness**a2, 1*b1, W(0)**b2, fluctuation strength*b3, sharpness_SD**

Standardized partial
regression coefficients
−0.65
0.06
−0.33
−0.33
0.69

Sharpness and the delay time of the maximum peak of the ACF, 1 , were significant predictive
variables for subjective salience for birdsongs in noise environments. The partial regression
coefficients of sharpness and 1 were negative and positive, suggesting that blunt birdsongs with lower
pitch are salient.
The width of the first decay of the ACF, W (0) , fluctuation strength, and sharpness_SD weresignificant predictive variables for subjective salience for insect songs in noise environments. The
partial regression coefficients of W (0) and fluctuation strength for insect songs were negative. Thesesuggests that sharper insect songs with lower fluctuation strength were salient. The positive partial
coefficient of sharpness_SD means insect songs with higher variation of sharpness are salient.

4. CONCLUSIONS
We examined the salience of birdsongs and insect songs in noise environments in terms of several
physical parameters. The results indicated that the birdsongs of Horornis diphone, which is commonly foundin Japan, and the insect songs of Meimuna opalifera, which is often heard in Japan in the summer, are themost salient birdsong and insect song. Sharpness and the delay time of the maximum peak of the ACF, 1,were significant predictive variables for birdsong salience. The width of the first peak of the ACF, W (0),fluctuation strength, and variation of sharpness were significant predictive variables for subjective salience
for insect songs.

ACKNOWLEDGEMENTS
This work was partly supported by a Grant-in-Aid for Scientific Research (B) (Grant No.
18H03324, 22H03916) from the Japan Society for the Promotion of Science.

REFERENCES
1. Soeta, Y. and Kagawa, H. Subjective preferences for birdsong and insect song in equal sound pressurelevel. Appl. Sci. 10, 849, (2020).
2. Ratcliffe, E., Gatersleben, B. and Sowden, P. T. Bird sounds and their contributions to perceived atten-tion
restoration and stress recovery. J. Environ. Psychol. 38, 221-228, (2013).
3. Ratcliffe, E., Gatersleben, B. and Sowden, P. T. Associations with bird sounds: How do they relate to perceived restorative potential? J. Environ. Psychol. 47, 136-144, (2016).
4. Axelsson, Ö., Nilsson, M. E., Hellström, B. and Lundén, P. A field experiment on the impact of sounds
from a jet-and-basin fountain on soundscape quality in an urban park. Landsc. Urban Plan. 123, 49-60,(2014).
5. Galbrun, L. and Calarco, F. Audio-visual interaction and perceptual assessment of water features used overroad traffic noise. J. Acoust. Soc. Am. 136, 2609-2620, (2014).
6. Kang, J., Aletta, F., Gjestland, T. T., Brown, L. A., Botteldooren, D., Schulte-Fortkamp, B., Lercher, P.,
van Kamp, I., Genuit, K., Fiebig, A., et al. Ten questions on the soundscapes of the built environment.
Build Environ. 108, 284-294, (2016).
7. De Coensel, B., Vanwetswinkel, S. and Botteldooren, D. Effects of natural sounds on the perception ofroad traffic noise. J. Acoust. Soc. Am. 129, EL148-EL153, (2011).
8. Jeon, J. Y., Lee, P. J., You, J. and Kang, J. Perceptual assessment of quality of urban soundscapes withcombined noise sources and water sounds. J. Acoust. Soc. Am. 127, 1357-1366, (2010).
9. Hong, J. Y. and Jeon, J. Y. Designing sound and visual components for enhancement of urban sound-scapes.
J. Acoust. Soc. Am. 134, 2026-2036, (2013).
10. Ou, D., Mak, C.M. and Pan, S. A method for assessing soundscape in urban parks based on the service
quality measurement models. Appl. Acoust. 127, 184-193, (2017).
11. Foundation for Promoting Personal Mobility and Ecological Transportation, Basic case study report on
movement support for traffic hub, 2009 (in Japanese).
12. Ministry of Land, Infrastructure, Transport and Tourism. Guidelines for improving the facilitation oftransportation for passenger facilities of public transportation, 2020 (in Japanese).
13. Sato, H., Morimoto, M. and Sato, H. Effect of noise and reverberation on sound localization of acoustic
guide signal for visually impaired persons in public spaces. Noise Control Eng. J. 62, 1-9, (2014).
14. Sato, H., Morimoto, M. and Sato, H. Perception of azimuth angle of sound source located at high elevation angle: Effective distance of auditory guide signal. Appl. Acoust. 159, 107084, (2020).
15. Soeta, Y. and Nakagawa, S. Prediction of optimal auditory signals using auditory evoked magnetic re-sponses. Build. Environ. 94, 924-929, (2015).
16. Soeta, Y., and Ariki, A. Subjective salience of birdsong and insect song with equal sound pressure level
and loudness, Int. J. Environ. Res. Public Health 17, 8858 (2020).
17. Hoth, D. F. Room noise spectra at subscribers' 268 telephone locations. J. Acoust. Soc. Am. 2, 499–504(1941).
18. Shimokura, R., and Soeta, Y. Characteristics of train noise in above-ground and underground stations
with side and island platforms. J. Sound Vib. 330, 1621–1633 (2011).
19. Scheffé, H. An analysis of variance for paired comparisons. J. Am. Statist. Assoc. 147, 381-400, (1952).
20. Sato, S. Statistical method of sensory test, JUSE Press: Tokyo, Japan, 1985. (in Japanese).
21. Nagasawa, S. Improvement of the Scheffe's method for paired comparisons. Kansei Eng. J. 3, 47-56,(2002).
22. Ando, Y. Auditory and visual sensations, Springer: New York, USA, 2009.
23. Soeta, Y. and Ando, Y. Neurally based measurement and evaluation of environmental noise, Tokyo:
Springer Japan; 2015.
24. Ando, Y. and Kurihara, Y. Nonlinear response in evaluating the subjective diffuseness of sound field. J.
Acoust. Soc. Am. 80, 833-836, (1986).
25. Sato, S. and Ando, Y. Apparent source width (ASW) of complex noises in relation to the interaural crosscorrelation function. J. Des. Archit. Environ. 2, 29-32, (2002).
26. Zwicker, E. and Fastl, H. Psychoacoustics: facts and models, Springer-Verlag: Berlin, Germany, 1999.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0623

Vocal problems of healthcare workers in China during the pandemicaSong Ziwei ; Pyoung-Jik Leeaa

Acoustics Research Unit, School of Architecture, University of Liverpool, Liverpool, L69 7ZN, UK 2

ABSTRACTThis study explored the vocal problems of healthcare workers in China during the pandemic. Firstly, online
questionnaire surveys were conducted in three hospitals in China. Healthcare workers from different
departments in hospitals completed the questionnaires. A total of 411 healthcare workers took part in thesurvey with different face mask requirements (N95 masks with face shields or surgical masks without faceshield). Second, semi-structured interviews were carried out with critical care nurses as a qualitative approach.
A total of 15 nurses have finished this survey so far. Lastly, voice-related parameters of the nurses from three
ICUs were measured. For this, a non-invasive accelerometer was attached to a participant’s neck during
working hours. Healthcare workers in the fever clinic showed the most serious voice problem during the
pandemic, followed by intensive care units. The most frequent voice symptom was ‘voice tiredness’ andfollowed by the ‘difficulty in being heard’.
Keywords: healthcare workers, vocal problems, Noise in hospitals, Pandemic

1. INTRODUCTION
Interpersonal communication has been severely affected during the COVID -19 pandemic.
Protective measures, such as social distancing and face masks, are essential to mitigate efforts against
the coronavirus [1], and pose challenges to daily face-to-face communication. Face masks muffle
sounds and cover facial expressions that ease comprehension during live communication [2]. For
example, successful communications between healthcare workers and patients are essential in
hospitals; however, the presence of face masks may reduce speech intelligibility and cause vocal
problems.
Several studies have highlighted the vocal problems of healthcare workers. For example, Heider
et al. [3] conducted a questionnaire survey and reported that healthcare workers of high-risk hospitalcare units are at risk of voice disorders. However, most studies were carried out in developed countries
and there was no attempt to explore the voice problems of the healthcare workers in developing
countries. In addition, the majority of previous studies performed questionnaire surveys and aqualitative approach was not adopted.
Therefore, this research aims to understand whether personal protective equipment such as facemasks affects vocal problems in hospitals during the pandemic in Chinese hospitals. First, aquestionnaire survey was conducted in three hospitals to investigate the vocal problems of thehealthcare workers. Second, semi-structured interviews were conducted to explore how healthcare
workers react to noise in hospitals. Lastly, voice monitoring was performed with the nurses inintensive care units.

2. METHODOLOGY
2.1 Sites
The online questionnaires were conducted in three hospitals (Sites A, B, and C) in China from
December 2021 to April 2022. Site A is the smallest and was recently rebuilt from the old factory. Site
B is the largest, while Site C is a part of the local university in Chongqing, China. The semi-structured
interviews were also carried out in August 2022 in the intensive care units (ICUs) of these threehospitals.

2.2 Participants
A total of 411 healthcare workers (162 from Site A, 133 from Site B, and 116 from Site C) wererecruited from three hospitals for online surveys. Table 1 shows the socio-demographic andprofessional characteristics of the participants. The participants were aged between 20-57 (mean 37.5,
SD 10.7) with different educational levels. Among them, 307 healthcare workers have worked in thehospitals for more than ten years and thirteen participants have worked for less than one year. Seventynine healthcare workers are from the fever clinic departments, including 56 volunteers from medical
universities or other out-of-town hospitals during the pandemic, while 77 participants worked in theintensive care units. During working hours, all the participants at the fever clinic departments wore
both N95 face masks (named for their ability to filter 95% or more of tiny 0.3-μm particles) and faceshields for more than 12 hours, whereas others wore surgical masks for about 8 hours (see Figure 1).
For the semi-structured interviews, 14 nurses from the ICU were recruited later.
Table 1 – Socio-demographic and professional characteristics of the participants
Site A

Site B

Site C

Total

Male

29

36

35

100

Female

133

97

81

311

20-25

12

5

3

20

25-30

18

47

33

98

30-35

104

76

67

247

>35

27

5

13

45

Nurse

113

87

74

274

Medical doctor

17

23

8

48

Technician

5

9

27

41

Therapist

7

3

2

12

Volunteer

20

11

6

37

<1

8

3

2

13

1-2

4

2

2

8

2-5

4

7

10

21

5-10

17

27

19

63

>10

130

94

83

307

Fever clinic

37

25

17

79

Intensive care unit (ICU)

35

19

23

77

Obstetrics and Gynaecology

7

6

3

16

Medical technology department

15

32

44

91

Clinical departments

75

50

21

146

Administration department

5

3

8

16

Surgical mask

137

108

96

341

N95+face shields

37

25

17

79

GenderAge (yrs.)

Role

Years of working in hospitals

Departments

Personal protective equipment (PPE)Figure 1 – Healthcare workers who wear personal protective equipment (PPE) in the fever clinic (left) andICU (right)
2.3 Questionnaire survey and interview
The questionnaire consists of three major parts: 1) basic information such as age and gender, 2)vocal problems in hospitals, and 3) perception of the acoustic environment. The participants wereasked to rate the experience of the vocal symptoms using a 5-point scale (1: never and 5: always). The
Voice Handicap Index (VHI-30) was also used to measure the self-perception of voice handicaps. In
addition, the participants were asked about noise sources, and they rated their perception of acoustic
environments using a semantic scale. Second, semi-structured interviews were carried out with ICU
nurses as a qualitative approach. The interview aimed to investigate 1) how the ICU nurses perceiveand react to the noise sources and 2) the effects of acoustic environments on nurses’ voices, job
satisfaction, and well-being. During the interviews, the participants were encouraged to freely express
their thoughts and experiences concerning vocal problems and acoustic environments in hospitals.

2.4 Voice monitoring
The voice monitoring was carried out for 4 hours during working hours using the Voice -Care device(PR.O. VOICE S.r.l, Turin, Italy). The Voice-Care device provides information on the fundamental
frequency (Hz), phonation time percentage (Dt%), and vocal sound pressure level (dB SPL).
Phonation time percentage is defined as the percentage of time spent phonating over the total recording
time. Before the monitoring, the device was calibrated in a quiet room in each hospital using areference microphone. In addition, the participants will be asked to perform a conversational task
(pre-monitoring). For this task, the nurses will be asked to speak to a listener, positioned at 1 m, about
a topic that is well known to them.

3. RESULTS
3.1

Questionnaire survey

Table 2 shows the types of face masks in different departments and the hours of wearing. The feverclinic has the longest hours of wearing the strictest mask (N95 with face shields). The experiences of
vocal-related symptoms are summarised in Table 3. The percentage in the table indicates thepercentage of respondents whose answers are greater than 3 on a 5-point scale. ‘voice tiredness’ is themost common symptom across all departments during the pandemic with 276 participants (67.2%).
The fever clinic showed more symptoms than others, followed by ICUs. Specifically, 72.2% ofparticipants in the fever clinic department reported ‘voice tiredness’ and 70.2% answered that they
had experienced ‘difficulty in heard’. Similarly, in ICUs, ‘voice tiredness’ and ‘difficulty in heard’ are
the most frequent symptoms, followed by ‘voiceless. On the other hand, in general departments, only
‘voice tiredness’ exceeded 60% and other symptoms were rarely reported .
Table 2 – Usage of face masks in different departments
Departments

N95

Face shields

Surgical mask

Fever clinic>12hrs>12hrs

N/A

ICU

N/A

N/A

8-12hours

Obstetrics and Gynaecology

N/A

N/A

8-12hours

Medical technology department

N/A

N/A

8-12hours

Clinical departments

N/A

N/A

8-12hours

Administration department

N/A

N/A

8-12hours

Table 3 – Voice-related symptoms in different departments
Fever clinic

General

ICU

departments

department

%

Mean

%

Mean

%

Mean

Hoarseness

50.6

2.6

44.7

2.3

40.3

2.4

Voice tiredness

72.2

3.3

65.1

2.9

68.8

2.8

Voiceless

62.0

2.9

37.7

2.3

67.5

2.8

Dryness in the throat

67.1

3.0

38.8

2.3

36.4

2.4

Sore throat when speaking

39.2

2.5

39.2

2.3

41.6

2.4

Aphonia

16.5

1.6

36.9

2.3

31.2

2.2

Clearing the throat

19.0

1.67

41.2

2.7

39.0

2.9

Difficulty in being heard

70.9

3.1

36.1

2.2

79.2

3.0

Persistent dry cough

24.1

1.9

42.8

2.3

39.0

2.3

Lump in the throat

16.5

1.8

29.8

2.1

37.7

2.3

Results of VHI-30 are listed in Table 4. The median values of the total VHI-30 in all departments
were 26.8 (less than 30); however, the score of the fever clinic was 42 due to the wearing of N95 facemasks with face shields.
Table 4 – Median scores for the total VHI-30 and VHI subscales
Total

Fever clinic

ICU

OthersTotal VHI-30

26.8

42.0

28.8

24.7

Functional subscale

9.7

17.3

11.7

8.0

Emotional subscale

6.3

10.3

6.3

6.0

Physical subscale

11.0

12.0

11.7

10.0

3.2

Semi-structured Interview

Fourteen ICU nurses have been interviewed so far and 14 will be more recruited. All the nurses
were females, and the average age was 32 years old (SD 7.1). The length of working in the ICU ranged
between 1 and 15 years with a mean of 7.9 years. Table 5 shows the demographic information of theparticipants and the questions raised in the interviews are listed in Table 6.
Table 5 - Demographic Information of the participants (N=14)
Characteristics
Age (mean ± SD)
Gender (N)FemaleMale
Length of working (in years; mean ± SD)
Length of working in ICU (in years; mean ± SD)
12-hour shift roster (N)8-hour shift roster (N)ICU
32 ± 7.1
140
10.6 ±7.7
7.9 ±4.8
9
5

Table 6 – A list of interview questions
Can you tell me what kind of noise you have heard in ICU?
How did you feel when you exposed (or heard) the noise?
Did any specific noises disturb you?
Have you had any issues with your voice while working in ICU?
Have you experienced any difficulties in understanding others’ voices?
How did you react to noise?
Do you think the noise in ICU affects your job satisfaction?
Do you think noises in ICU affect psychological well-being?
3.2.1 Noise sources
The participants listed several noise sources such as equipment, patients, other nurses, and noisefrom outside. Most participants mentioned the noise from equipment (e.g., life monitor).
P10: “In ICU, most noises come from the equipment. The breathing machine and life monitor
make me unwell.”
P7: “When we are working in the nurse station, the nurses in patient wards usually shout out to
let us clearly hear the situation.”
3.2.2 Perception
Most perceptions of noise sources were negative, and they made the participants feel annoyed andanxious; however, several nurses said that they were used to the noise sources.
P10: “Sometimes you need to concentrate on a problem, but the noise always makes you
anxious.”P3: “Although we can hear a lot of noise such as the life monitor and patients’ voices, we are
not feeling annoyed. Maybe we are used to it, or maybe we do not have many patients.”
3.2.3 DisturbanceThe noise in ICU made them speak louder so it affects their communications with the patients andother staff. Also, the noise led to sleep disturbance when they wanted to have a break.
P2: “The noise of equipment everywhere still affects our communication.”P4: “Sometimes we are tired and want to have a break. However, the noise around us made usvery hard to fall asleep.”
3.2.4 Intervening conditions
It was found that business and the seriousness of the patients affect the perception of noise sources
and stress.
P10: “But it's especially bad when you're busy. Too much work makes me feel annoyed by thenoise. Sometimes you need to concentrate on a problem, but the noise always makes you
anxious.”P8: “But when we are busy, noise from equipment and patient complaints makes me crazy.”

3.3 Vocal monitoring
As listed in Table 7, the nurses in Site B showed higher mean values of the vocal sound pressurelevel throughout the entire monitoring session than those of site A and C. Across the sites, the mean
voice levels of the nurses during the monitoring were significantly higher than tho se in the premonitoring session. For instance, the increase in mean sound pressure levels in Site A was 4.7 dB.
Table 7 - Mean Values of voice-related parameters across three hospitals. Numbers in parentheses represent
standard error. F0 and Dt represent fundamental frequency and phonation time percentage, respectively
Site A
Site B
Site C
N=15
N=15
N=15
EM (Entire monitoring)
Mean (SE)Mean (SE)Mean (SE)SPL_median(dB)

76.7

80.0

78.5

SPL, mean(dB)

74.5

77.6

76.7

SPL, SD (dB)

2.6

3.8

3.4

F0, mean (Hz)

246.9

380.3

276.7

F0, SD(Hz)

60.0

62.7

61.3

Dt [%]

7.1

11.6

9.3

Pre-monitoring (PM)Mean (SE)Mean (SE)Mean (SE)SPL_median(dB)

74.8

73.0

74.5

SPL, mean(dB)

69.9

72.9

73.9

SPL, SD(dB)

1.8

3.2

3.4

F0, mean (Hz)

226.5

159.9

197.6

F0, SD(Hz)

25.8

24.2

31.8

Dt [%]

37.5

40.3

38.1

EM-PM

Difference

Difference

Difference

SPL_median(dB)

1.9

7.0

4.0

SPL, mean(dB)

4.7

4.6

2.8

SPL, SD (dB)

0.8

0.6

0.0

F0, mean (Hz)

20.5

220.4

79.0

F0, SD(Hz)

34.2

38.5

29.6

Dt [%]

-30.5

-28.7

-28.8

SUMMARY
The present study explored the vocal problems of healthcare workers in three different hospitals
in China during the pandemic. Questionnaires revealed that the healthcare workers in the fever clinic
have the most serious voice problem during the pandemic, followed by ICUs. The most frequent voice
symptom was ‘voice tiredness’ and ‘difficulty in being heard’. Vocal problems in hospitals are
significantly affected by the type of masks during the pandemic. The median value of the total VHI30 score indicated that the voice handicap associated with voice disorder was most significant in the
fever clinic department, followed by ICU. The VHI-30 score in the general department was minimal
during this research. It was also found that the mean values of voice SPLs were much affected by thetype of face mask and the nurses increased their voice levels while they are working in the ICUs .

REFERENCES[1] Howard, J., Huang, A., Li, Z., Tufekci, Z., Zdimal, V., van der Westhuizen, H. M., ... & Rimoin,
A. W. (2021). An evidence reviews of face masks against COVID-19. Proceedings of the National
Academy of Sciences, 118(4), e2014564118.
[2] Thomas, F., Allen, C., Butts, W., Rhoades, C., Brandon, C., & Handrahan, D. L. (2011). Does
wearing a surgical facemask or N95-respirator impair radio communication?. Air medical Journal,30(2), 97-102.
[3] Heider, C. A., Álvarez, M. L., Fuentes‐López, E., González, C. A., León, N. I., Verástegui, D.
C., ... & Napolitano, C. A. (2021). Prevalence of voice disorders in healthcare workers in the universa lmasking COVID‐19 era. The Laryngoscope, 131(4), E1227-E1233.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0650

AN EXPERIMENTAL INVESTIGATION OF CHANGE IN HUMAN
BRAIN WAVES DUE TO HETEROGENEOUS TRAFFIC NOISE
Manish MANOHARE1, Bhavya GARGi, Rajasekar Eii and Manoranjan PARIDAiii
1,3

Department of Architecture and Planning, Indian Institute of Technology, Roorkee, India

2 Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India
4

Department of Civil Engineering, Indian Institute of Technology, Roorkee, India

ABSTRACT
Traffic noise pollution is a major environmental concern leading to several health issues. Countries
like India experience great diversity in urban noise climate due to heterogeneity in traffic conditions.
Health impact due to such environmental noise climates hence needs contextual investigation. This
paper investigates the change in human brain waves due to traffic noise exposure.
A listening experiment was conducted in a quiet chamber with 30 healthy participants without
hearing difficulty. The human brain response to various traffic noise exposure levels was recorded
using Electroencephalogram (EEG) measurements. Thirty noise signals sampled from New Delhi,
India covering major land use and traffic scenarios, formed the stimuli for listening experiments.
The results show the change in brain waves concerning the change in loudness of the noise scenario.
The major changes are observed in the frontal area of the human brain. The theta and alpha waves
have a major impact due to traffic noise exposure. Excessive honking and overall temporal variationin the traffic noise have a larger impact on subjects' comfortable state of mind. Results of the study
can complement the self-reported health impact studies conducted in similar contexts.
Keywords: Traffic noise pollution, electroencephalography (EEG), psychoacoustic indicator,
noise exposure

1. INTRODUCTION
Noise pollution is one of the significant environmental issues impacting the health of humans (1).
Traffic noise pollution is one of the major contributors to the overall noise pollution in urban areas(2). Noise pollution in developing nations like India is one of the major concerns. The high level ofdiversity in traffic, different speeds, acceleration, the combination of motorised and non-motorised
vehicles, non-adherence to the lanes and excessive honking leads to emission of noise level above thesafety standards (3). These heterogeneous traffic conditions lead to a noise climate with higher noiselevels. It is studied that in Indian cities majority of the location have noise levels higher thanprescribed by the pollution control board (4). As per WHO, it is estimated that noise pollution causes
different auditory and non-auditory health impacts (5). These health effects include hearing loss,
tinnitus, annoyance, cognitive impairment, hypertension, cardiovascular issues and sleep deprivation(6).
In the Indian scenario, average traffic noise level is more than 65 dB of the prescribed standards.
In one of the recent self-reported health surveys, it is observed that more than 6.3 % of the surveyed
population is affected by noise and other related issues (7).
It has been observed that the self-reported surveys don't represent the impact of noise exposure on
humans in an effective way. It is required to effectively understand the effect of noise exposure on
1

mmanish@ar.iitr.ac.in, 2 bgarg1@ee.iitr.ac.in , 3 raj@ar.iitr.ac.in; 4 m.parida@ce.iitr.ac.inhuman physiology and psychology. Recent studies have attempted to perform laboratory assessments
to analyse the effect of traffic noise. Specific techniques such as EEG, ECG, fMRI, GSR, EMG,
respiration rate, and Eye Blinking are a few methods which are to be used to assess the change inphysiological signals due to noise exposure. EEG is one of the techniques which captures changes inthe electrical activity of human brain in response to external stimuli. Studies have attempted to analyse
the effect of traffic noise exposure, music preferences and classification and prediction of emotions
elicited by different audio signals (8–10).
Multiple studies have attempted to correlate changes in emotions due to exposure to different audio
signals. However, correlation between psychoacoustic indicators and brain waves is rarely identified.
Through an EEG-based listening experiment, the study attempts to identify the relation betweenchange in psychoacoustic indicators and the change in brain waves at different frequency bands.
In this paper, the relation between traffic noise and EEG indicators is established to see the effectof noise on human brain waves. Traffic noise scenarios in the heterogeneous transportation system are
identified as input stimuli, and EEG signals are collected as a response to brain waves.

2. METHOD
2.1 Noise data collection
Traffic noise data was collected at thirty locations across Delhi city. The locations comprise ofvery noisy streets to quiet streets spread across different street types and land uses. Class 1 soundlevel meter was used to record the equivalent noise levels (LAeq ). Audio-visual data was recorded
using a binaural microphone and an omnidirectional camera. Binaural audio was recorded in the formof wave files, which were used for the listening experiment. The noise measurement was done per
ISO 1996-1:2016 and ISO 13213-2:2018 (11,12).
2.2 Calibration of the sound pressure levels for listening experiment
In a listening experiment, it is necessary to present the equal noise levels through an audio stimulus
which are observed on site. Accurate and reliable calibration is required to replicate the actual scenario
and to maintain ecological validity. The sample audio stimulus in the dataset is considered for thecalibration of sound pressure level, which acted as a reference audio stimulus. The LAeq level for aselected stimulus is obtained through a Class 1 sound level meter (Delta Ohm). In a quiet chamber, aflat response earphone (Etymotic ER4) driven by Audio Interface (SSL2) is placed over an artificial
head (KU100) to present the reference audio. The output audio file provided by the artificial head is
examined for the received sound pressure level. The difference in LAeq between the reference fileand LAeq measured by the artificial head is checked using MATLAB SPL meter toolbox. In order toremove this difference, the gain of stimulus is manually adjusted using audio interface until it reaches
the actual LAeq level provided by the reference file.
2.3 Listening experiment
A listening experiment was conducted at the Indian Institute of Technology, Roorkee, India. In thisexperiment the calibrated noise stimuli were presented to the participant. A quiet chamber with abackground noise level below 40 dBA was used for the listening experiment. A total of 30 participants(19 males and 11 females) with an age group of 20 to 35 years participated in the experiment. All theparticipants were university students without any hearing issues (self-reported).
An experiment was divided into three sets. In each set, participants were given ten traffic noise
stimuli of 15 seconds each, followed by a silence of 30 seconds as a resting period. During theexperiment, Emotiv EEG headset was used to record brainwaves as a response to traffic noise stimuli.
Breaks were provided between each set, and participants were allowed to take a rest in between thesets. Several studies have employed a similar methodology to examine the effects of urban noise on
individuals in controlled settings [12–14].

Figure 1: A: The experimental setup during the listening experiment. B: The location of electrodes of the
Emotiv EEG headset.
2.4 EEG Data processing and feature extraction.
EEG data was acquired using 14 channels EEG headset at a sampling rate of 128 Hz. Raw data of
EEG was in the range of 5 to 100 microvolts. This raw EEG data consists of multiple muscle artefacts,
eyeblinks etc. Raw EEG data was processed to remove this unwanted information, followed byextracting features from the processed data. These features were used to build a relationship withaudio features.
The session-long EEG recording was segmented for the baseline, and each stimuli data was
extracted for each subject. This created a processed dataset with 30 EEG files, each having 14 EEG
channels and 30 stimuli.
Raw EEG data were processed with the Bandpass filter with a lower cut-off frequency of 0.5 Hz
and an upper cut-off frequency of 50 Hz, as the conventional range of EEG is 0.5-50 Hz. Fifth-ordermoving mean filter was used to smoothen the raw EEG wave. The smoothened EEG wave was further
processed for the artefact removal Independent Component Analysis (ICA) method (16).
EEG features were extracted from the processed raw data using the discrete wavelet transform(DWT) technique (17). Eighth order DWT filtering technique was used, resulting in 8 wavelets out of
which the first 3 signals were noise signals, and the following 5 frequency bands, namely Gamma (3050 Hz), Beta(13-30 Hz), Alpha(8-13 Hz), Theta(4-8 Hz) and Delta(0.5-4 Hz). For each frequency band,the relative power is calculated for each participant corresponding to each sound stimuli.
Power and relative power (RP) can be analyzed in the time-frequency domain and calculated using
Eqs. (1) and (2), respectively. Band power (x) returns the average power in the input signal, x. If x isa matrix, then band power computes the average power in each column independently.
Power of signal x"#$

𝑝𝑜𝑤𝑒𝑟(𝑥) = *+"#%𝑥(𝑡)! *

(1)

Relative power of signal x&'()*(,)

𝑅𝑃(𝑥) = $'"./ &'()*(2)

2.5 Psychoacoustic indicators
These signals include different urban street scenarios ranging from quiet streets to noisy streetswith lots of honking. The length of each signal was 15 sec. Collected binaural wave file signals extract
the psychoacoustic indicators using MATLAB Audio Toolbox. These parameters include Loudness(N) ISO 523-1, Sharpness (S) DIN45692, Fluctuation strength (F), Roughness (R) and Psychoacoustic
annoyance (PA) defined by Zwicker and Fastl.
Psychoacoustic indicators are used to calculate the Psychoacoustic annoyance using the following
equation:#
𝑃𝐴~𝑁! %1 + (𝑤"# + 𝑤$%*(3)

Where,
N5 is the 5th percentile in sones,
WS = (S-1.75).(0.25.log10 (N5 +10)) for S>1.75, where s is sharpness in acum,
WFR=(2.18/((N5 )^0.4)).(0.4.F+0.6.R) where F is fluctuation strength in vacil and R is roughness inasper.

3. RESULTS AND DISCUSSION
1.1 Characteriation of the soundscape scenarios.
The section describes the variation of psychoacoustic indicators for different soundscapes stimuli.
Table (1) presents the statistical summary of psychoacoustic indicators of 30 soundscape scenarios.
Figure (2) shows the variation in the level of identified traffic noise scenarios. It is observed that the
N ranges from 9.73 sones (S16) to 48.19 (S26) sones with an average of 29.44 sones. The first and 3rdquartile range of the loudness is between 21.53 sones to 29.44 sones, explaining that the 50 percentof the dataset is between this range of loudness. Sharpness ranges between 1.31 (S17) to 1.85 (S26)
acum, with an average of 1.63 acum and 1st and 3rd quartile range of 1.58 & 1.70 acum respectively.
Fluctuation strength among all signals is minimum at (S1) 0.011 vacils and maximum at (S19) 0.062
vacils. Average level of fluctuation strength is reported as 0.035 vacils, whereas 1st and 3rd quartile
range are between 0.02 and 0.04 vacils respectively. Roughness of the signal is highest at (S20) 0.134
aspers, lowest at (S11) 0.020 and an overall average of 0.098 aspers. It is observed that loudness and
PA are closely associated with each other. The maximum PA is noted at (S30) 53.75, the minimum at(S16) 15.20 and the average PA of 36.47. The 1st and 3rd quartile range for PA is 27.51 and 45.39
respectively.
Table 1: Statistical summary for psychoacoustic indicators of soundscape scenarios.
Loudness - N

Sharpness -Fluctuation

Roughness –Psychoacoustic(sones)S (acum)Strength – F

R (aspers)

Annoyance – PA(vacils)
Min

9.74

1.31

0.01

0.02

15.20

1st Qu

21.53

1.58

0.02

0.08

27.51

Median

28.05

1.63

0.03

0.10

36.70

Mean

29.44

1.63

0.03

0.09

36.47

3rd Qu

37.34

1.70

0.04

0.11

45.39

Max

48.19

1.85

0.06

0.13

53.75

Figure 2: Boxplot presenting the variation of psychoacoustic indicators across all soundscape scenarios.

Figure 3: The variation of psychoacoustic indicators for scenarios S16 and S26 over 15 sec, along with thephotograph of each scenario.
To identify the impact of two distinct soundscape stimuli on the human brain waves, locations S16
and S26 are considered for the analysis. Figure (3) presents the variation of psychoacoustic indicatorsin spectral and temporal domain. Stimuli S16 is a silent location recorded in the residential zone withvery minimal movement of traffic, and less honking. Whereas, stimuli S26 is the loudest soundscapescenario with the high movement of traffic volume at a signalized junction with lots of honking.
Location S16 has a maximum loudness level of around 16 sones, and S26 have maximum loudness of
50 sones. The sharpness of stimuli S16 is lower and does not fluctuate much when compared with S26.
The high frequency content of S26 high very high when compared with S16. This is due to high traffic
noise and engine acceleration. Considering these two scenarios distinct scenarios, the change in
relative power is analyzed by averaging the RP of all participants across all 14 electrodes.
Figure (4) presents the change in relative power across all electrodes as an EEG response to thesoundscape stimuli S16 and S26. It is observed that there is a significant increase in relative powerwith a change in the overall loudness of the scenario. Stimuli associated with high annoyance levels
reported an increase in RP of alpha and theta band. Variation in beta band is observed in occipital andtemporal regions, but no significant relation is identified. In comparison, gamma band reported asignificant change in frontal and occipital lobes of the brain.

Figure 4: Scalp topological distributions for S16 and S26 noise stimuli showing the change in the relative
power of EEG signal.
3.1 Relation between Audio features and EEG features.
The two-tailed Pearson correlation test is conducted to analyse the relationship between change in
psychoacoustic indicators and change in the relative power of brain waves at different electrodelocations. Relative power for all thirty subjects is averaged together for each stimulus across allelectrodes. A correlation test is conducted to determine how the change in soundscape can lead tovariation in human brain waves. These fluctuations at different lobes can further provide insight into
the change in listeners' emotional states. Table 2 presents the correlation results between thepsychoacoustic indicators and relative power at different frequency bands.
Table 2: Correlation results showing the relation between psychoacoustic indicators and fluctuation of brain
waves at different frequencies.
Frequency

Electrode

Band

Max

Loudness

N5

Sharpnessloudness

Fluctuation

Roughness

Strength

RP (q)

F7

0.29

0.33

0.34

0.54**

0.22

-0.04

RP (q)

FC5

0.18

0.32

0.30

0.59**

0.32

-0.30

RP (q)

P7

0.11

0.15

0.16

0.26

0.40*

-0.14

RP (g)

O1

0.03

-0.06

-0.04

0.09

0.38*

0.00

RP (q)

O2

-0.17

-0.13

-0.11

0.38*

0.06

-0.14

RP (a)

O2

0.45*

0.41*

0.43*

-0.13

0.06

0.00

RP (q)

T8

0.17

0.13

0.12

0.38*

0.20

0.04

RP (q)

FC6

0.31

0.23

0.27

0.62**

0.35

0.00

RP (q)

F4

0.17

0.09

0.12

0.36*

0.38*

-0.12

RP (q)

F8

0.21

0.25

0.26

0.46*

0.27

0.02

0.39*

-0.20

AF4
0.35
0.35
0.37* 0.28
RP (q)** represents correlation is significant at the 0.01 level (2-tailed)* represents correlation is significant at the 0.05 level (2-tailed)

It is noted that the N is positively correlated with RP (a) at the O2 electrode, with r=0.41 (p<0.05).

Also, the 5th percentile of loudness (N5) is positively correlated with the fluctuation of RP (q) in thefrontal lobe (AF4) with r=0.37 (p<0.05).
The sharpness of the input signal and change in RP (q) in the frontal lobe exhibit a significant
relationship. The correlation (r) between sharpness and RP(q) at F7, FC5 and FC6 were 0.54, 0.59
and 0.62 respectively (p<0.01). Also, correlation coefficient between sharpness and RP (q) at O2, T8,
F4 and F8 was 0.38, 0.38, 0.36 and 0.46, respectively, (p<0.05).
The fluctuation strength of the signal has a significant relationship with the RP (g) and RP (q) at
the occipital and frontal lobe, respectively. The RP (g) at O1 is positively related with F (r=0.38 and(p<0.05)). The RP (q) at F4 and AF4 is related with fluctuation strength with r=0.37 and 0.39,
respectively, (p<0.05). In contrast, no significant correlation is observed between the roughness of thesignal and the change in RP of electrodes at all EEG frequency bands.
Finally, the relation between psychoacoustic annoyance and change in RP is analysed. The
correlation analysis shows that PA is correlated with RP (q) at AF4 with r=0.37, (p<0.05). Also, theRP (a) at O2 reported a strong positive correlation with PA with r=0.42, (p<0.05).
Change in relative power is mainly observed in the theta and alpha band. The change in RP ismainly noted in the frontal and occipital lobes of the brain. The variation in loudness reflects thechange alpha band at the occipital lobe and theta band in the frontal lobe. Whereas the sharpness ismainly associated with the change in theta band across the frontal and occipital lobes of the brain.
Also, the change in fluctuation strength leads to a change in the gamma band at the occipital lobe andthat band in the frontal lobe.
Traffic noise influences the RP of EEG signals. The theta, alpha and gamma band show a relation
with fluctuation of brain waves. Multiple research studies have attempted to derive the associatedchange in emotion with a variation of brain waves. The changes in theta band are associated withchange in the participants' relaxed mood, leading to drowsiness and annoyance. The change in Alphaband in occipital lobe and frontal lobe is mainly associated with the change in mental activity, andcomfortable state of humans (18). Based on these studies it can be inferred that the traffic noise
scenario is responsible for change in comfortable state of humans.

4. CONCLUSION
The paper summarizes the effect of traffic noise scenarios on human brain waves. The
psychoacoustic parameters have shown a significant relationship with the change in brain signals.
Parameters such as sharpness and the fluctuation strength of the signal have reported a significant
relation with change in brain waves. The major changes are observed in the frontal and occipital lobes,
at the theta and alpha band. The analysis reveals that overall loudness is one of the basic parameters
for identifying changes in human brain waves. Apart from loudness the frequency content of the signal
and the temporal variation of the signal are other indicators which can be employed to associate thechange in amplitude of the EEG signals due to traffic noise exposure.
The results can be employed to associate with self-reported surveys on noise health impact. Further,
the variation in brain waves and its relation with change in noise exposure can help to develop an
emotion prediction model for different soundscape scenarios.
Acknowledgement: The authors would like to express sincere gratitude to every participant forparticipating in the experiment.
Funding information: The research work is supported by the Prime Minister's Research
Fellowship, Ministry of Education, India (ID 2800113).
Conflict of interest: The authors state no conflict of interest.

5. REFERENCES
1. Stansfeld SA, Matheson MP. Noise pollution: Non-auditory effects on health. Vol. 68, British
Medical Bulletin. 2003. p. 243–57.
2. Khan J, Ketzel M, Kakosimos K, Sørensen M, Jensen SS. Road traffic air and noise pollution
exposure assessment – A review of tools and techniques. Vol. 634, Science of the Total
Environment. Elsevier B.V.; 2018. p. 661–76.
3. Vijay R, Sharma A, Chakrabarti T, Gupta R. Assessment of honking impact on traffic noise inurban traffic environment of Nagpur, India. Journal of Environmental Health Science andEngineering. 2015 Feb 1;13(1).
4. Pathak V, Tripathi BD, Mishra V kumar. Evaluation of traffic noise pollution and attitudes of
exposed individuals in working place. Atmospheric Environment. 2008 May;42(16):3892–8.
5. World Health Organization. Burden of Disease from Environmental Noise Burden of disease fromenvironmental noise Quantification of healthy life years lost in Europe [Internet]. 2011. Available
from: www.euro.who.int
6. Manohare M, Rajasekar E, Parida M, Vij S. Bibliometric analysis and review of auditory and nonauditory health impact due to road traffic noise exposure. Vol. 9, Noise Mapping. Walter deGruyter GmbH; 2022. p. 67–88.
7. Partheeban P, Karthik K, Navin Elamparithi P, Somasundaram K, Anuradha B. Urban road traffic
noise on human exposure assessment using geospatial technology. Environmental Engineering
Research. 2021 Sep 30;27(5):210249–0.
8. Li TM, Chao HC, Zhang J. Emotion classification based on brain wave: a survey. Vol. 9, Humancentric Computing and Information Sciences. Springer; 2019.
9. Ashtaputre-Sisode A. Emotions and brain waves. The International Journal of Indian Psychology.
2016;3(2):14–8.
10. Er MB, Çiğ H, Aydilek İB. A new approach to recognition of human emotions using brain
signals and music stimuli. Applied Acoustics. 2021 Apr 1;175.
11. International Organization for Standardization. ISO 1996-1:2016 Acoustics — Description,measurement and assessment of environmental noise — Part 1: Basic quantities and assessment
procedures. 2016.
12. International Organization for Standardization. ISO/TS 12913-2:2018 Acoustics —Soundscape — Part 2: Data collection and reporting requirements. 2018.
13. Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, et al. EEG-based emotion recognitionin music listening. IEEE Transactions on Biomedical Engineering. 2010;57(7):1798–806.
14. Suhaimi NS, Mountstephens J, Teo J. EEG-Based Emotion Recognition: A State-of-the-Art
Review of Current Trends and Opportunities. Vol. 2020, Computational Intelligence and
Neuroscience. Hindawi Limited; 2020.
15. A Novel framework of EEG-based user identification by analyzing music-listening behavior _Enhanced Reader.
16. varinen AH. Fast and robust fixed-point algorithms for independent component analysis. Neural
Networks, IEEE Transactions on. 2008;10(3):2008.
17. Salem O, Naseem A, Mehaoua A. Epileptic seizure detection from EEG signal using Discrete
Wavelet Transform and Ant Colony classifier. 2014 IEEE International Conference on
Communications, ICC 2014. 2014;3529–34.
18. Lal SKL, Craig A. A critical review of the psychophysiology of driver fatigue [Internet]. Vol.
55, Biological Psychology. 2001. Available from: www.elsevier.com/locate/biopsycho

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0702

Enhancing Well-being through Acoustic Environments
Alaa ALGARGOOSH 1
1

College of Design, Imam Abdulrahman Bin Faisal University, Saudi Arabia

ABSTRACT
Research that studies the impact of acoustics on health and well-being tends to focus on the harmful effects
of noise. However, reducing the negative noise effect is not the only way to design acoustically supportive
environments. Previous research revealed that sound can create a positive impact. For instance, introducing
natural environmental sounds to the soundscape can enhance well-being. Nevertheless, further research isneeded to understand the acoustic characteristics of supportive environments. Research in music
demonstrated that listening to music can effectively transform the state of mind and direct it to a positive
affective state depending on its musical characteristics. Considering that acoustic environments have thepotential to create a similar effect, how can we design restorative acoustic environments, and what are theacoustic characteristic of such spaces? This research discusses the impact of acoustic environments onemotions and the acoustic characteristics associated with enhancing the emotional impact. Further, it offersa direction for future research on supportive acoustic environments through exploring the architecturalfeatures that contribute to the mentioned effect.
Keywords: Acoustic Environment, Well-being, Emotion, Soundscape, Restoration.

1. INTRODUCTION
1.1

Restorative soundscapes

Many researchers studied the negative aspects of acoustic environments and their impact on people[1]. Reducing harmful sound effects is a step forward; however, there is a need to create more positive
acoustic environments that support well-being [2]. Therefore, more attention has been given recently
to the potential positive impact of soundscapes.
Cain et al. [2] analyzed the emotional influence of soundscapes based on two dimensions that
included "calmness" and "vibrancy," through the Positive Soundscape Project.
Medvedev et al. [3] took a step further by analyzing the physiological response to study the impactof acoustic environments on health and well-being and found that soundscapes that have been reported
positively have the potential to be restorative. They also indicated that linking the characteristics ofthe acoustic environment with subjective evaluation is essential.
Li and Kang [4] studied the restorative potential of soundscapes by analyzing the physiologicalresponse and subjective evaluation. The results showed a strong physiological response correlatedwith the subjective evaluation; however, the subjective response was insufficient to predict thephysiological response accurately.

1

asalgargoosh@iau.edu.sa

Ratcliffe et al. [5] adopted the stress recovery theory to study the restorative potential of bird
sounds and found that the aesthetic properties are linked to the restorative impact of the sound, andthe acoustic properties and familiarity are linked to emotional impact. They also recommended taking
advantage of natural environments that create positive acoustic experiences.
The studies mentioned above focused mainly on outdoor environments. However, a study by
Subiza-Pérez et al. [6] shed light on the importance of considering the restorative potential of indoor
environments. Although the studied outdoor spaces with elements from nature, such as urban parks
and forests, received higher ratings for restoration, one-third of the participants rated the indoor
studied environments as more restorative.
Torresin et al. [7] investigated the link between soundscapes and well-being and found that
comfortable soundscapes are correlated with well-being.
A systematic review of Aletta et al. [8] stated that research that studied the links between
soundscapes and positive health impact showed that soundscapes rated positively were linked withfaster restoration from stressors and better self-reported health conditions. Furthermore, Aletta andKang [9] indicated that researchers are active in investigating the impact of the acoustic environmenton health and well-being and encourage further research in studying the positive impact instead ofjust focusing on reducing the negative aspects.
1.2

The Emotional Impact of Acoustic Environments

Soundscape research focuses on the perception of the acoustic environments, and recent studies
extend beyond annoyance to address the emotional impact [10].
Pätynen and Lokki [11] stated that some of the acoustic characteristics, such as envelopment,
strength, and resonance, are linked to the listeners' emotional response and called for further
investigation to understand the relationship between the acoustic parameters and their impact onemotions.
Tajadura-Jiménez et al. [12] studied the effect of perceived room size on emotions and reported
that small rooms were linked to feeling more safe and calm compared to large rooms.
Lawless and Vigeant [13] studied the impact of the indoor acoustic environment on emotions byusing functional magnetic resonance imaging (fMRI) and found that listening to an auralized soundthat received the highest rating was linked to an activation of a brain region related to the anticipationof reward.
Algargoosh et al. [14] indicated that acoustic environments have the potential to increase theemotional impact depending on the acoustic characteristics and listeners' familiarity.
Researchers indicate that pleasant soundscapes can be uneventful (calm) or eventful (exciting andfascinating) [15]. Previous research indicates that although soundscapes can impact emotions, unlike
music, acoustic environments are usually not designed with the intention or consideration of evoking
emotional responses [10]. So, how can we design fascinating soundscapes that can impact emotions?
What are the acoustic characteristics of such soundscapes?
Linking emotional response indicators with acoustic characteristics of an acoustic environment
and investigating the architectural features contributing to creating such an environment is essential
to provide guidelines to design spaces that enhance well-being. The following sections will address
both mentioned aspects.

2. THE LINK BETWEEN THE EMOTIONAL IMPACT AND THE ACOUSTICCHARACTERISTICS
Previous research indicated that Early Decay Time (EDT) and Sound Strength (G) values at low
frequencies are positively correlated with emotional impact in worship spaces [16].
A study of historical worship spaces known for their acoustic quality, including Hagia Sophia and
Suleymaniye, showed that EDT values significantly decrease at frequencies above 500 Hz [17].
Another study that analyzed the acoustics of historical sites used for worship or rituals showed thatthe reverberation time (T30) values are higher at low frequency. In most cases, it also drops
significantly after 500 Hz [18]. A similar result was observed when analyzing the data available in theroom examples of Odeon software (through CAHRISMA project), showing that Suleymaniye, andSelimiye, have high EDT values at low frequencies, which drop significantly above 500 Hz (Figure
1).
Research indicated that Long EDT at low frequencies is linked to modal resonance [19], andresonance quality has also been linked to emotional impact [16]. Previous research recommended
further investigation of resonance at low frequencies and exploring patterns of modal resonance [19].
The standard followed by many researchers, which focuses on reporting the values of the acoustic
parameters at 1000 Hz, ignores low frequencies, which may lead to missing crucial acoustic
information that is significant to the acoustic experience [16]. Till [19] explained that such standards
were developed for specific building types, making them unsuitable for different types such asarchaeological sites. Considering that low frequencies are correlated with emotional impact. The same
argument can be applied to spaces that are designed to enhance emotions and well-being.

Figure 1: Early Decay Time (EDT) for Suleymaniye, Selimiye, and Hagia Sophia at different sound receiver locations using
sound source (S1).

The studies mentioned above shed light on the importance of analyzing low frequencies whenstudying the emotional impact of acoustic environments. However, caution should be taken whenusing simulation, given that geometrical room acoustics models might not be accurate at low
frequencies [16,19]. To illustrate, Figure 2 shows EDT results for Hagia Sophia using fieldmeasurements and simulations (the data is provided by Odeon software embedded examples titled
CAHRISMA project).

Figure 2: Simulated and measured Early Decay Time (EDT) for Hagia Sophia.

Since there is a link between the positive emotional impact of an acoustic environment and theacoustic parameters such as EDT, Sound strength, and resonance at low frequencies, the next question
is: What are the architectural features that contribute to the desired acoustic impact?

3. THE LINK BETWEEN ACOUSTIC CHARACTERISTICS AND
ARCHITECTURAL FEATURES
Many spaces mentioned in the previous section share common architectural elements, such as subvolumes and curved surfaces. So, how are the mentioned elements impacting the acoustic parameters
at low frequencies? Answering this question can open the possibility for more creative architecturaldesigns that are not limited to such elements but could share some of their general characteristics that
produce specific acoustic environments.
As mentioned above, long EDT values are linked to modal behavior and resonance. Resonancecauses an amplification at specific frequencies and is influenced by factors that include spacegeometry and sound source location [20]. Previous research provided a method to analyze such
amplification by considering sound source location and surface materials [21]. However, it is essential
to analyze geometry's impact to understand the architectural elements' role. This research contains apreliminary analysis of a case study of a worship space that includes the architectural elements linked
to enhancing the emotional impact in previous studies to analyze the contribution of sub-volume andcurved surfaces. Field measurements have been conducted using an acoustic camera which utilizes
beamforming technology as a receiver and a balloon pop as a sound source. The initial results showthat the sub-volumes contributed to the amplification of frequencies below 500 Hz.
Future work will focus on developing guidelines to design spaces that enhance well-being byfurther investigation of the design aspects that amplify frequencies linked to well-being.

ACKNOWLEDGEMENTS

Many thanks to Professor Mojtaba Navvab from the University of Michigan for his valuable
feedback.

REFERENCES
[1]

Kang

J,

Schulte-Fortkamp

B.

Soundscape

and

the

Built

Environment

2016.

https://doi.org/10.1201/b19145.
[2]

Cain R, Jennings P, Poxon J. The development and application of the emotional dimensions of asoundscape. Appl Acoust 2013;74:232–9. https://doi.org/10.1016/J.APACOUST.2011.11.006.

[3]Medvedev O, Shepherd D, Hautus MJ. The restorative potential of soundscapes: A physiologicalinvestigation. Appl Acoust 2015;96:20–6. https://doi.org/10.1016/J.APACOUST.2015.03.004.

[4]

Li Z, Kang J. Sensitivity analysis of changes in human physiological indicators observed in soundscapes.
Landsc Urban Plan 2019;190:103593. https://doi.org/10.1016/J.LANDURBPLAN.2019.103593.

[5]

Ratcliffe E, Gatersleben B, Sowden PT. Predicting the Perceived Restorative Potential of Bird Sounds
Through

Acoustics

andAesthetics.

EnvironBehav

2018;52:371–400.

https://doi.org/10.1177/0013916518806952.
[6]

Subiza-Pérez M, Pasanen T, Ratcliffe E, Lee K, Bornioli A, de Bloom J, et al. Exploring psychologicalrestoration in favorite indoor and outdoor urban places using a top-down perspective. J Environ Psychol
2021;78:101706. https://doi.org/10.1016/J.JENVP.2021.101706.

[7]

Torresin S, Albatici R, Aletta F, Babich F, Oberman T, Stawinoga AE, et al. Indoor soundscapes at homeduring the COVID-19 lockdown in London – Part I: Associations between the perception of the acoustic
environment,

occupantś

activity

andwell-being.

Appl

Acoust2021;183:108305.

https://doi.org/10.1016/J.APACOUST.2021.108305.
[8]

Aletta F, Oberman T, Kang J. Associations between Positive Health-Related Effects and Soundscapes
Perceptual Constructs: A Systematic Review. Int J Environ Res Public Heal 2018, Vol 15, Page 2392
2018;15:2392. https://doi.org/10.3390/IJERPH15112392.

[9]

Aletta F, Kang J. Promoting Healthy and Supportive Acoustic Environments: Going beyond the Quietness.
Int

J

EnvironRes

Public

Heal

2019,

Vol

16,

Page

4988

2019;16:4988.

https://doi.org/10.3390/IJERPH16244988.
[10]

Fiebig A, Jordan P, Moshona CC. Assessments of Acoustic Environments by Emotions – The ApplicationofEmotion

TheoryinSoundscape.

Front

Psychol

2020;11:3261.

https://doi.org/10.3389/FPSYG.2020.573041/BIBTEX.
[11]

Pätynen J, Lokki T. Concert halls with strong and lateral sound increase the emotional impact of orchestra
music. J Acoust Soc Am 2016;139:1214–24. https://doi.org/10.1121/1.4944038.

[12]

Tajadura-Jiménez A, Larsson P, Väljamäe A, Västfjäll D, Kleiner M. When Room Size Matters: Acoustic
Influenceson

Emotional

Responses

toSounds.

Emotion2010;10:416–22.

https://doi.org/10.1037/a0018423.
[13]

Lawless MS, Vigeant MC. Investigating the emotional response to room acoustics: A functional magnetic
resonance imaging study Effects of test method and participant musical training on preference ratings ofstimuliwithdifferent

reverberation

times.

Cit

J

AcoustSoc

Am

2015;138:417.

https://doi.org/10.1121/1.4933232.
[14]

Algargoosh A, Soleimani B, O'Modhrain S, Navvab M. The impact of the acoustic environment on humanemotion and experience: A case study of worship spaces. Build Acoust 2022:1351010X211068850.
https://doi.org/10.1177/1351010X211068850.

[15]

Axelsson Ö, Nilsson ME, Berglund B. A principal components model of soundscape perceptiona). J Acoust
Soc Am 2010;128:2836. https://doi.org/10.1121/1.3493436.

[16]

Algargoosh A. Aural Architecture as Affect: Understanding the Impact of Acoustic Environments on
Human Experience. University of Michigan, 2021.

[17]Sü Gül Z, Çalışkan M, Tavukçuoğlu A, Xiang N. Assessment of Acoustical Indicators in Multi-domed
Historic Structures by Non-exponential Energy Decay Analysis. Acoust Aust 2018;46:181–92.
https://doi.org/10.1007/S40857-018-0136-9/FIGURES/9.

[18]

Murphy DT. Archaeological acoustic space measurement for convolution reverberation and auralization
applications. Proc. 9th Int. Conf. Digit. Audio Eff. (DAFx-06), Montreal, Canada: 2006.

[19]

Till R. Sound Archaeology: A Study of the Acoustics of Three World Heritage Sites, Spanish Prehistoric
Painted Caves, Stonehenge, and Paphos Theatre. Acoust 2019, Vol 1, Pages 661-692 2019;1:661–92.
https://doi.org/10.3390/ACOUSTICS1030039.

[20]

Heller E. Why you hear what you hear: an experiential approach to sound, music, and psychoacoustics.
Princeton University Press; 2013.

[21]

Algargoosh A, Navvab M, Granzow J. A method for analyzing room modal response using auralization.
Appl Acoust 2022;195:108859. https://doi.org/10.1016/J.APACOUST.2022.108859.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0992

The Correlation Between Misophonia and Autonomous Sensory
Meridian Response-Induced Negative Mood Changes
Yu-Lin WU1; Hirohito M. KONDO3; I-Fan LIN1,2*
12

Taipei Medical University, Taipei City, 110, Taiwan

Department of Occupational Medicine, Shuang Ho Hospital, New Taipei City, 235, Taiwan
3

School of Psychology, Chukyo University, Nagoya, Aichi, 466-8666, Japan

ABSTRACT
Autonomous sensory meridian response (ASMR) describes a sensory experience in which certain stimuli
elicit tingling sensations. The tingling sensations are usually accompanied by a sense of happiness andrelaxation. Some people find that ASMR has therapeutic effects such as relieving symptoms. Despite these
positive effects of ASMR, ASMR has been found to be correlated with misophonia, a phenomenon in which
certain sounds elicit strong negative emotions. In this study, we conducted an online survey which aimed to
examine the relationship between mood changes induced by ASMR, including positive and negative moodchanges, tingling sensations and misophonia. A sample of 394 participants joined the survey, and their moodchange responses and tingling sensation responses were measured immediately after watching each video(including 6 ASMR videos and 1 non-ASMR video). After they watched all videos, participants completed
the Misophonia Questionnaire. Results showed that misophonia is significantly correlated with participants’negative mood changes but not their positive mood changes induced by ASMR videos. These findings
suggest that the correlation between ASMR and misophonia can be attributed to the connection between
auditory perception and negative emotion.
Keywords: ASMR, misophonia, emotion

1. INTRODUCTION
Autonomous sensory meridian response (ASMR) is a sensory experience wherein certain stimuli(i.e., ASMR triggers) elicit tingling sensations that originate from the scalp and then spread down toother body parts (1). In addition to the tingling sensations, previous research has reported thatwatching ASMR videos increases positive emotions (instilling a sense of calmness or happiness) andrelaxation and reduces stress levels (2, 3). However, not everyone who watch ASMR videos canexperience these positive effects. It has been reported that responses such as positive mood changes,
decreased heart rate, and stress reduction while watching ASMR videos only occur in individuals who
can experience ASMR (2, 3). Individuals who cannot experience ASMR have described ASMR videos
as weird and stressful, making them less engaged in ASMR videos (4).
Misophonia is a phenomenon in which certain sounds elicit emotional responses. However, unlike
ASMR, misophonia results in strong negative emotions, such as disgust and anger. Individuals who
suffer from misophonia often find sounds distressing, including those produced when other people
eat, chew, cough, or sniffle (5, 6). Previous research found an overlap between the triggers for ASMR
and the triggers for misophonia. For example, the sounds produced when people eat and whisper
trigger both ASMR and misophonia (6). In addition, compared with matched controls, individuals who
experienced ASMR scored significantly higher on the Misophonia Questionnaire (7, 8). Moreover,previous research found that ASMR and misophonia often coexist. In a group of misophonicparticipants, 49% reported that they experienced ASMR (9). Similarly, approximately 43% of those
who experienced ASMR reported that they experienced misophonia (10).

To investigate the relationship between ASMR and misophonia, we enrolled participants to an
online survey, in which they were asked to watch ASMR videos and report their emotional responses
and if they experienced any tingling sensations. Once the participants reported their emotional
response after watching each video, their answers were categorized as either positive or negative.
Next, the participants were asked to complete the Misophonia Questionnaire (6). Since misophonia
refers to negative emotional responses toward specific sounds, we hypothesized that misophonia iscorrelated with ASMR-induced negative emotion changes but not positive emotion changes. A
previous study reported that individuals’ experiences may affect their ASMR (11). Therefore, we
separated the participants into the ASMR-naïve and ASMR-aware groups and hypothesized that thecorrelation between misophonia and ASMR is stronger in the ASMR-naïve group than in the ASMRaware group.

2. METHODS
A total of 394 participants (187 men and 207 women; age, 23.37 ± 3.91 years) joined this online
survey; 291 of the 394 (74%) participants reported that they knew ASMR. The current study wasapproved by the Institutional Review Board of Taipei Medical University, and all participants signed
the online consent form before they started the study.
In the first part of this survey, the participants were asked whether they knew ASMR or not. Then,the participants were asked to watch seven 1-min-long videos involving the following topics:
scratching, tapping, whispering, eating, ear cleaning, kinetic sand, and domino falldown. Because
domino falldown video is not a ASMR trigger, it was considered the baseline condition in this study.
Other 6 videos are considered ASMR triggers. These videos were selected from YouTube, and weedited each video to one minute long. After watching each video, the participants were asked to report
if they experienced any mood change. If their answer was “yes,” they were asked to indicate the typeof mood change they experienced. If positive mood changes (e.g., calmness and enjoyment) werereported, 1 point was assigned to the positive mood change score; if negative mood changes (e.g.,
anxiousness and irritation) were indicated, 1 point was assigned to the negative mood change score.
Next, the participants were asked whether they experienced any tingling sensation while watching thevideo. If the answer was “yes,” 1 point was added to the tingling sensation score. The overall moodchange, positive mood change, negative mood change, and tingling sensation scores ranged from 0 to
6 (for the 6 ASMR videos). The scores of all the participants are presented in Table 1. After watching
all seven videos, the participants were asked to complete the Misophonia Questionnaire (6). The
questionnaire is composed of three subscales. The misophonia symptom subscale includes sevenquestions that asks whether the participant experiences adverse feelings towards certain sounds. The
misophonia emotional and behavioral responses subscale includes ten questions that asks about theparticipant’s emotional and behavioral response related to misophonia. Both subscales requireparticipants to answer on a 5-point Likert scale. The Misophonia Questionnaire total score (MQ) usedin following analysis is the sum of the misophonia symptom subscale and the misophonia emotionaland behavioral subscale. Finally, the misophonia severity scale includes one questions regarding theoverall severity of misophonia (from 0-10 point).
Table 1 – Participants’ demographic information and scores for the Misophonia Questionnaire, moodchange response, and tingling sensation response.
Frequency

Mean (S.D.)

Min

Max

Age

23.37 (3.91)

20

42

MQ

37.43 (8.53)

1261

Mood changes score

4.18 (1.67)

06

Positive mood changes score1.90 (1.59)

06

GenderMale

187

Female207

Negative mood changes score2.27 (1.49)

0

6

Tingling sensation score2.63 (1.75)

06

3. RESULTS
Figure 1 presents the participants’ mood change responses to each video. In general, >50% of all
the participants experienced positive or negative mood changes after watching the seven videos (69%for the scratching video, 65% for the tapping video, 74% for the whispering video, 69 % for the eating
video, 79% for the ear-cleaning video, 64% for the kinetic sand video, and 69% for the domino
falldown video). Among those participants who experienced mood changes, a greater proportion ofthem experienced negative rather than positive mood changes after watching the scratching (50% vs.
20%), whispering (45% vs. 30%), eating (36% vs. 32%), and ear-cleaning (48% vs. 31%) videos. By
contrast, more participants experienced positive rather than negative mood changes after watching thetapping (33% vs. 31%), kinetic sand (44% vs. 19%), and domino falldown (36% vs. 32%) videos.
When comparing the 6 ASMR videos presented in this study to the baseline condition (i.e., the domino
falldown video), no significant differences were noted in mood change responses (t(5) = 1.24, p =0.27 for positive mood changes; t(5) = 1.19, p = 0.29 for negative mood changes).

Figure 1 – Percentage of the positive and negative mood change responses reported by the participants
towards each video.
As presented in Figure 2, 48%, 29%, 45%, 37%, 69%, 34%, and 22% of the participants reported
that they experienced tingling sensation responses after watching the scratching, tapping, whispering,eating, ear-cleaning, kinetic sand, and domino falldown video, respectively. Compared with thosewatching the baseline condition (i.e., the domino falldown video), significantly more participants
reported experiencing tingling sensations while watching the 6 ASMR videos (t(5) = 3.79, p = 0.01).

Figure 2 – Percentage of the tingling sensation responses reported by the participants towards each video.
Table 2 presents the results of tingling sensation responses, positive and negative mood changes,
and MQ scores for the 6 ASMR videos observed in all participants (N = 394), in ASMR-aware
participants (N = 291) and in ASMR-naïve participants (N = 103). The results of the Wilcoxon signedrank test revealed that compared with the ASMR-aware participants, the ASMR-naïve participants had
significantly lower scores for tingling sensations and positive mood changes but significantly higherscores for negative mood changes; However, there was no between-group differences for MQ.
Table 2 – Mean and standard deviation (in parentheses) of tingling sensation scores, positive mood change
scores, negative mood change scores, and MQ scores in all participants, ASMR-aware participants, and
ASMR-naïve participants. Wilcoxon tests (W and p-value) were conducted to compare the scores in the
ASMR-aware participants and in the ASMR-naïve participants.
ScoresAll participants

ASMR-aware

ASMR-naïve

(N = 394)

participants

participants(n = 291)(n = 103)Wp

Tingling

2.63 (1.75)

2.77 (1.74)

2.23 (1.72)

12325< 0.01

Positive mood change

1.90 (1.59)

2.10 (1.63)

1.38 (1.37)

11060< 0.01

Negative mood change

2.27 (1.49)

2.13 (1.46)

2.66 (1.51)

17849< 0.01

MQ

37.43 (8.53)

37.39 (8.67)

37.52 (8.19)

14832

0.88

Spearman’s correlation analysis was conducted to investigate the correlation between ASMRinduced mood changes and misophonia. The results showed that misophonia was significantly
correlated with negative mood changes (r = 0.21; p < 0.001) but not with positive mood changes (r =0.04; p = 0.43). There was no between-group difference in correlations between misophonia andpositive mood changes. In both ASMR-aware and ASMR-naïve groups, misophonia was not correlatedwith positive mood changes (r = 0.04 and p = 0.52 in the ASMR-aware group; r = 0.05 and p = 0.62

in the ASMR-naïve group). Similarly, in both groups, misophonia was significantly correlated withnegative mood changes (r = 0.20 and p = 0.008 in the ASMR-aware group; r = 0.27 and p = 0.006 in
the ASMR-naïve group).
On the other hand, the correlations between tingling sensation responses and positive and negative
mood changes showed between-group differences: the significant correlation between tingling
sensation responses and positive mood changes was only observed in the ASMR-aware group, and thesignificant correlation between tingling sensation responses and negative mood changes was only
observed in the ASMR-naïve group. In addition, the significant correlation between tingling sensation
responses and MQ was only observed in the ASMR-naïve group.
Table 3 – Results of Spearman’s correlation analysis in all participants, ASMR-aware participants, and
ASMR-naïve participants. Values in bold indicate significant correlations.
All participants (N =ASMR-aware

ASMR-naïve

394)

participants (n = 291)

participants (n = 103)

Tingles and positiver = 0.14

r = 0.17

r = –0.07

mood changes

p = < 0.01

p < 0.01

p = 0.50

Tingles and negativer = 0.11

r = 0.11

r = 0.24

mood changes

p = 0.03

p = 0.07

p = 0.01

MQ and positive moodr = 0.04

r = 0.04

r = 0.05

changesp = 0.43

p = 0.52

p = 0.62

MQ and negative moodr = 0.21

r = 0.20

r = 0.27

changesp < 0.01

p < 0.01

p < 0.01

MQ and tinglingr = 0.13

r = 0.10

r = 0.26

sensations

p < 0.01

p = 0.09

p < 0.01

4. DISCUSSION
The aim of this study was to investigate the relationship between ASMR-induced mood changes
and misophonia. In addition to mood changes, we examined tingling sensation responses to ASMR
videos. Our results revealed that although not all the participants experienced tingling sensations andmood changes while watching the ASMR videos, thus corroborating that ASMR is a multidimensional
phenomenon (12).
Although previous studies have reported associations between ASMR and positive mood changes(1, 2, 3), in our study, a large proportion of the participants reported negative emotional responses
toward ASMR videos. This may be attributable to ASMR-induced positive effects (stress reduction
and calmness) observed among the participants who experienced ASMR but not among those naïve to
ASMR. Our results revealed significant between-group differences (ASMR-aware and ASMR-naïve
groups) in correlations between mood changes and tingling sensation responses. Specifically, ASMRinduced tingling sensations were positively correlated with positive mood changes in the ASMRaware group and with negative mood changes in the ASMR-naïve group, indicating that positive
emotions elicited through ASMR may be acquired through learning. Thus, individuals’ preferences to
ASMR stimuli may be substantiated by increased experience to various ASMR stimuli (11). In
addition, these findings were in line with previous studies reporting that individuals who can andcannot experience ASMR may have different responses while watching ASMR videos (3, 4).
Regarding the association between misophonia and ASMR, in both the ASMR-aware and ASMRnaïve groups, a positive correlation was noted between negative mood changes and misophonia but
not between positive mood changes and misophonia. This indicated that negative mood changes
induced by ASMR and misophonia may share similar underlying mechanisms. A paper proposed ahypothesis stating that the cross-activation of the primary auditory cortex and insula leads to thecoexistence of ASMR and misophonia (5). However, this hypothesis was theoretically driven andlacked empirical evidence. Therefore, future studies are warranted to investigate neurophysiologicalmechanisms underlying ASMR and misophonia to determine the correlation between the two
conditions.
Although the correlations between misophonia and mood changes did not show differences
between the ASMR-aware participants and ASMR-naïve participants, the correlation between moodchanges and tingling sensations and the correlation between MQ and tingling sensations did show
between-group differences. In summary, these results indicate that although the correlation between
MQ and ASMR-induced negative mood change was similar for all participants, how they attributed
positive or negative mood changes could differ based on their past experiences. To be specific, ASMRaware participants might tend to attribute positive mood changes to ASMR due to the correlation
between positive mood changes and ASMR-induced tingling sensations, and ASMR-naïve participants
might tend to attribute negative mood changes to ASMR and considered the ASMR-induced tingling
sensations associated with the responses related to misophonia.

ACKNOWLEDGEMENTS
This work was financially supported by Taipei Medical University (grant no. TMU108-AE1-B38)
and JSPS KAKENHI grants (nos. 20H01789 and 22K18659).

REFERENCES
1. Barratt, E. L., & Davis, N. J. (2015). Autonomous Sensory Meridian Response (ASMR): a flow-like mental
state. PeerJ, 3, e851.
2. Poerio, G. L., Blakey, E., Hostler, T. J., & Veltri, T. (2018). More than a feeling: Autonomous sensory
meridian response (ASMR) is characterized by reliable changes in affect and physiology. PLoS
ONE, 13(6), e0196645.
3. Eid, C. M., Hamilton, C., & Greer, J. M. (2022). Untangling the tingle: Investigating the association
between the Autonomous Sensory Meridian Response (ASMR), neuroticism, and trait &amp; stateanxiety. PLoS ONE, 17(2), e0262668.
4. Kovacevich, A., & Huron, D. (2019). Two studies of autonomous sensory meridian response (ASMR):
The relationship between ASMR and music-induced frisson. Empirical Musicology Review, 13(1-2), 3963.
5. McGeoch, P. D., & Rouw, R. (2020). How everyday sounds can trigger strong emotions: ASMR,
misophonia and the feeling of wellbeing. BioEssays, 42(12), 2000099.
6. Wu, M. S., Lewin, A. B., Murphy, T. K., & Storch, E. A. (2014). Misophonia: incidence, phenomenology,
and clinical correlates in an undergraduate student sample. Journal of Clinical Psychology, 70(10), 9941007.
7. McErlean, A. B. J., & Banissy, M. J. (2018). Increased misophonia in self-reported autonomous sensory
meridian response. PeerJ, 6, e5351.
8. Tada, K., Hasegawa, R., & Kondo, H. M. (in press). Sensitivity to everyday sounds: ASMR, misophonia,
and autistic traits. Japanese Journal of Psychology
9. Rouw, R., & Erfanian, M. (2018). A large‐scale study of misophonia. Journal of Clinical Psychology, 74(3),453-479.
10. Barratt, E. L., Spence, C., & Davis, N. J. (2017). Sensory determinants of the autonomous sensory
meridian response (ASMR): understanding the triggers. PeerJ, 5, e3846.
11. Koumura, T., Nakatani, M., Liao, H.-I., & Kondo, H. M. (2021). Dark, loud, and compact sounds induce
frisson. Quarterly Journal of Experimental Psychology, 74(6), 1140-1152.
12. Roberts, N., Beath, A., & Boag, S. (2019). Autonomous sensory meridian response: Scale development
and personality correlates. Psychology of Consciousness: Theory, Research, and Practice, 6(1), 22.