PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

VR Environment-based Evaluation of Impact Factors on the Urban
Soundscape Recognition
Rosa SEO1; Hyun In JO1; Jin Yong JEON1
1

Hanyang University, South Korea

ABSTRACT
To investigate the impact of soundscape spatial change factors in a pleasant urban environment, we evaluateda soundscape in a virtual reality (VR) environment and investigated the audiovisual impact factors on thesoundscape. First, nine evaluation points were selected around Area A, and three-minute sound environments
were selected at each point using a 360-degree camera (Insta360) and a SoundField (SPS200) microphone toinvestigate changes in the sound pressure level and frequency characteristics. Subjective responses, such as
overall satisfaction, soundscape evaluation factors, and landscape evaluation factors, were evaluated in threeenvironments (audio, visual, and audio with visual) based on the sound environment measurement data. To
realize the immersive environment, the subjects were allowed to move freely in VR by reflecting the directionof the sound source and head rotations. Assessments of the level of immersion and sense of reality were used
to verify the experimental environment. The experiment results showed that, in terms of the soundscapeevaluation factors, overall satisfaction decreased as traffic noise increased and human sounds decreased. As
for the landscape evaluation factors, the vegetation factor was the most dominant; as the green area increased,
the overall satisfaction increased despite the noise factor. As a result, as the visual factors related to naturalelement, such as green area, increased, the auditory complaint factor was controlled and satisfaction towards
the urban soundscape improved.
Keywords: Soundscape, Landscape, Audio-visual interaction, Expectation, Soundscape design quality

1. INTRODUCTION
With the negative effects of urban noise on health emerging as a social issue, the urban acoustic
environment has begun to be considered an important factor for sustainable and healthy urban life. In
this context, the concept of the soundscape has emerged. It focuses on the actual human perception of
the acoustic environment (1, 2). ISO 1291-1 defines soundscape as the "acoustic environment asperceived or experience and / or understood by a person or people, in context ." In this context, manystudies have investigated the correlations between acoustic indicators and soundscape descriptors invarious urban spaces (parks, squares, and streets) (3, 4).
It is important to investigate the contexts formed by various complex factors , including suchnonacoustic factors as the function of the space, types of environment, and social background tointerpret the soundscape. Context also includes factors that, in a broad sense, could affect theperception of soundscapes. Therefore, psychological factors, such as expectations—as well as
physical factors constituting space, such as visual factors and auditory factors—and social
backgrounds should be examined (5, 6).
There are many studies on soundscape quality and audio-visual interaction based on the fact thatthe early concept of soundscape originated from the landscape and is closely related to the visual. As
a result, recognition models considering audio-visual interaction have been proposed to interpret theurban soundscape. However, until now, most of them have focused on the auditory property, and thedetailed analysis model considering the detailed factors of the landscape is insufficient (7, 8).
1

rosa824@hanyang.ac.kr
best2012@hanyang.ac.kr
1
jyjeon@hanyang.ac.kr
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Assessment methodology soundscape research is mostly based on fieldwork methods, such as
soundwalk and narrative interviews, for reasons of high ecological validity. When the surrounding
environment needs to be controlled, 2D photos and Photoshop techniques are used in the laboratoryenvironment (9, 10). However, this method of providing visual stimuli has the limitation that it does
not sufficiently reflect the real sense of space and the actual field. To solve this probl em, virtual reality(VR) technology has recently been introduced (11-13). It has been confirmed that the similarity ishigh when the subjective evaluation test results are compared with the actual field in the soundscapeevaluation in an environment implemented with VR. Therefore, it is necessary to develop more practical research on audio-visual interaction by applying this VR technique. (8, 14)Therefore, in this study, audiovisual interaction was investigated for soundscape and landscapeperception in urban environments by applying VR technology, and the relationship between
soundscape quality and the urban environment was examined. In conclusion, an analytical model for
overall satisfaction is newly proposed.

2. MANUSCRIPT FORMAT
2.1 Case study area
To investigate audio-visual influences through auditory evaluation in a laboratory environment,
nine evaluation sites located in Seoul were selected, including various urban contexts ( see Figure 1).
The points where various noise sources such as traffic, people, and birds exist, including open space,green space, and a waterfront space (river), far away, were visually selected. A route was set up froma point where the traffic was densely close to the road, where the noise was the highest, to a point
surrounded by a building and relatively quiet.
The A-weighted sound pressure level (L Aeq ) was calculated to determine the sound strengthcharacteristics of each evaluation point. L Ceq-Aeq, which is calculated as the difference between the Aweighted and the C-weighted SPL, is presented, showing the relative low-frequency characteristics
with respect to the sound spectral contents. L A10-A90 was calculated to investigate the temporalvariability of the sound environment. The L Aeq mean value of the evaluation points was very widelydistributed in the range of 57.2–79.4 dBA. In addition, the difference between the maximum andminimum values of L A10-A90 and LCeq-Aeq exceeded 10 dB, respectively, which includes the variation
range of the sound environment that can occur in a typical urban area.
G

Figure 1. Pictures of the nine evaluation locations for the experiment2529

2.2 VR soundscape environment
The measurements were conducted during the daytime (10 a.m. to 2 p.m.) in September 2018 toimplement the VR evaluation environment in the laboratory environment. A spherical panoramic
camera (Insta 360 camera) was used for recording for 3 min at each evaluation point. At the sametime, an ambisonic microphone (Soundfield SPS 200) and a portable sound recorder (Mixpre-6,
Sounddevices) were used for measuring the audio environment data and recorded with A -format firstorder ambisonic (FOA). Both the spherical camera and the ambisonic microphone were measured ata height of 1.6 m. A-weighted sound pressure levels (LAeq) were measured using a 1/2 -inch
microphone (G.R.A.S AE46) and an AS-70 portable sound level meter (RION) to calibrate the soundpressure level.
A 360° camera image recorded with six channels was implemented with a head-mounted display(HMD). For the implementation of headphone-based 3D stereo sound, the sound source recorded with
the A-format FOA was converted into B-format FOA using Unity software and down-mixed with abinaural track. Then, the head tracker mounted on the VR device was used to reflect the real-time
direction of the sound source according to the head rotation of the subject. To calibrate the soundpressure level at each evaluation point, nine sound sources generated from the VR were recorded in asemianechoic chamber using a head and torso simulator (Brüel & Kjær 4100) and adjusted using
Adobe Audition software version 1.5.
2.3 Experimental design
To evaluate the audio-visual environment interaction, the soundscape in three evaluation
environments was evaluated: (1) only audio environment, (2) only visual environment, and (3) audio visual environment. Experiments were conducted in a semianechoic chamber where background noise
was approximately 25 dBA. Open-type headphones (Sennheiser HD-650) were used for evaluation,
and VIVE Pro 2 (HMD) was used for visual information.
The questionnaire for the evaluation of the audiovisual interaction of the soundscape wascomposed of five parts. At each evaluation point, dominant sound sources (traffic, human s, birdsongs,
music, and wind), dominant visual elements (vehicles, buildings, roads, open, green, people, and sky),soundscape quality (pleasant, unpleasant, eventful, uneventful, exciting, monotonous, calm, andchaotic), landscape quality (comfortable, uncomfortable, harmonious, disharmonious, natural,
artificial, open, closed, wide, narrow, orderly, and disorderly), and overall satisfaction were evaluatedon a five-point Likert scale.
2.4 Procedure
Each subject underwent soundscape evaluation in three different evaluation environments, and theorder of evaluation environment was presented as only audio, only visual, and audio -visualenvironment. Each participant participated in one evaluation environment per day and was evaluatedfor a total of three days. To facilitate the response to the questionnaire, Unity software was used to
select the response directly in the VR with the VIVE controller. The order of the evaluation soundsources was sequentially evaluated for nine stimuli from H1 to H9 along the path, and intervals
between evaluation sound sources were 10 s. To solve the physical discomfort of the subjects due towearing the HMD for a long time, the experiment time of each evaluation environment was notexceeded for 30 min, and the rest time was provided at the request of the subject.

3. RESULTS
3.1 Perception of audio-visual indicator
Figure 2 shows the dominant and audible preference for each evaluation point in only audio andaudio-visual environments. Overall, traffic sound was dominant, and human sound was high in theroad, around the building, and in the moving route. Bird sound was also high in a well -organizedgreen area, such as a promenade and a park. As a result of two-way analysis of variance (ANOVA)
analysis, there was a significant difference of sound and presence according to the presence or absenceof visual information for traffic, humans, birds, and wind. It was shown that humans, birds, and wind
are better recognized, but not traffic, in an audio and visual environment.

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Figure 2. Dominant sound sources of each location

Figure 3 shows the dominant visual factors and the preference for each visual factor in each of theevaluation points in the only visual and the audio-visual environment. The distance from the road was
the most influential factor. The closer to the big road, the higher the awareness of vehicles. The twoway ANOVA analysis and the simple main analysis of the interaction between the visual informati on
and the audio information showed significant differences for the building, green, people, and sky
elements (p < 0.01). In the audio-visual environment, the perception of building, green, and sky
elements was lower, and for green it increased.

Figure 3. Dominant visual elements of each location

3.2 Semantic differential test
Principal components analysis was performed on eight semantic expression words for soundscapeand 12 semantic expression words for landscape in all evaluation environments. The Kaiser–Meyer–
Olkin measure of sampling adequacy was adequate, and Bartlett's test of sphericity was evident in all
evaluation settings (p < 0.01). First, soundscape quality was classified as pleasantness andeventfulness in the only-audio and audio-visual environments. In the case of landscape quality, overall
quality, regularity, and spatial impression were observed in the only-visual environment, andnaturalness was found to be a new factor in the audio-visual environment.

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3.3 Overall satisfaction
Figure 4 shows the overall satisfaction with audio information and visual information in different
evaluation environments. There was a difference in satisfaction according to the evaluation
environment, and the difference was statistically significant (F (2,807) = 61.401, p < 0.01). Overallsatisfaction was the highest in the visual-only environment, and satisfaction was low in the case ofadditional audio.

Figure 4. Picture of the nine evaluation locations for the experiment

4. DISCUSSION
4.1 Audio-visual interaction
Table 1 shows the correlation between perceived sound, perceived visual component, andsoundscape and landscape components derived from PCA analysis to examine the interaction between
perceived soundscape and landscape in urban space.
First, traffic has a negative effect on landscape components, and birds have a positive effect on theoverall impressions. Pleasantness showed a positive correlation with the overall impression of thelandscape and how well the city is organized and visually organized, and how many natural elements
are provided. Eventfulness showed a negative correlation with regularity. Therefore, the more the city
is visually congested, the more the sound event is recognized in the space.
Table 1. Correlation coefficients between perceived soundscape indicators and perceived landscapeindicators (Bold p < 0.01, underscore p < 0.05)
Sound sources

Visualelements

LandscapeTraffic

Human

Bird

Wind

Music

Vehicle

0.72

0.07

-0.22

0.05

-0.12

-0.56

0.05

Building

0.29

0.16

-0.04

0.13

-0.04

-0.21

0.08

Road

0.63

0.06

-0.19

0.06

-0.04

-0.53

-0.03

Open

-0.04

0.04

0.18

0.01

-0.06

0.02

-0.12

Green

-0.07

0.05

0.25

0.15

0.17

0.08

-0.11

People

0.09

0.43

0.04

0.18

-0.01

-0.01

0.05

Sky

0.01

0.22

-0.05

0.22

-0.14

-0.01

0.24

Overall quality

-0.28

0.05

0.30

0.11

0.01

0.50

0.17

Regularity

-0.22

0.01

0.17

0.12

0.03

0.30

-0.24

0.09

0.10

-0.01

0.12

-0.03

-0.06

0.11

-0.32

-0.09

0.23

0.08

0.12

0.26

-0.11

component Spatial impression
Naturalness

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Soundscape component
Pleasantness Eventfulness

4.2 Soundscape recognition model
To evaluate the contribution of audio-visual information to overall satisfaction and the contributionof soundscape and landscape components, multiple linear regression analysis was performed in each
evaluation environment, and the results are shown in Tables 2 and 3. R2 of the total regression model
was statistically significant (p < 0.05).
First, the interesting thing about audio-visual information is that the subjects focus more on thecharacteristic information of the space in the environment provided with both visual and auditory
information than the environment provided with only visual or auditory information.
Also interesting in terms of the soundscape and landscape component is that the new factor ofnaturalness is more influential in the environment where audio information is added. This means thatwhen the subjects are added with a sound element in recognizing the landscape of the city, they
consider how natural and artificial the space is rather than the recognition of the spatial feeling of thecity. In addition, overall, the landscape element has a higher contribution than the soundscape element,
and it can be seen that the subjects are more influenced by the visual parts when they evaluate thesatisfaction of the city.
Table 2. Standardized regression coefficients from multiple linear regression analysis for overallsatisfaction using sound and visual elements in different experimental setups(Bold p < 0.01, underscore p < 0.05)
Visual effect

Audio effectInteraction

Only Audio

Audio+Visual

Only Visual

Audio+Visual

Audio+Visual

R2

0.14

0.25

0.15

0.21

0.31

Traffic

-0.27

-0.29

-

-

-0.02

Human

0.12

0.03

-

-

0.05

Bird

0.12

0.31

-

-

0.29

Wind

-0.14

-0.01

-

-

0.02

Music

0.02

0.07

-

-

0.04

Vehicle

-

-

-0.28

-0.36

-0.30

Vehicle

-

-

0.06

-0.02

-0.03

Building

-

-

-0.05

-0.1

-0.07

Road

-

-

0.17

0.07

0.03

Open

-

-

0.12

0.11

0.04

Green

-

-

-0.08

0.02

-0.01

People

-

-

0.05

-0.06

-0.04

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Table 3. Standardized regression coefficients from multiple linear regression analysis for overallsatisfaction using soundscape and landscape components in different experimental setups(Bold p < 0.01, underscore p < 0.05)
Visual effect

Audio effectInteraction

Only Audio

Audio+Visual

Only Visual

Audio+Visual

Audio+Visual

2

0.45

0.32

0.49

0.49

0.51

Pleasantness

0.66

0.56

-

-

0.21

Eventfulness

0.12

-0.05

-

-

-0.05

Overall quality

-

-

0.62

0.55

0.45

Regularity

-

-

0.28

0.29

0.22

Spatial impression

-

-

0.16

0.05

0.06

Naturalness

-

-

-

0.32

0.25

R

5. CONCLUSIONSIn this study, audio-visual interaction of soundscape and landscape was investigated through a VR
environment in the laboratory to improve user satisfaction through the sound and visual environmentof urban space. A regression model was proposed based on audio-visual elements — soundscape andlandscape components — and it was confirmed that satisfaction prediction through a combination ofsoundscape and landscape can be used to design and improve the urban space. The model using audiovisual elements showed a somewhat lower explanatory power, but it was found that the bird's soundand the visual part of the vehicle were the main factors of urban design that had a significant effecton satisfaction. The model 51% using the soundscape and landscape component shows a highexplanatory power. At the same time, for the overall satisfaction of the city, the model seeks to
improve the naturalness and overall quality by adding appropriate vegetation element s while visually
adjusting it, and improving pleasantness was effective. The audio-visual interaction of the soundscapeand landscape evaluation found in this study and the overall satisfaction model based on it arepresented as guidelines for improving the satisfaction of the new design of the city and can be usedeffectively.

ACKNOWLEDGEMENTS
This work was supported by a grant the Science and Technology Amicable Relationship (STAR)
program, from the National Research Foundation of Korea (NRF) grant funded by the Korea
government (MSIT) (2018K1A3A1A21043775)

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