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

Research on soundscape identification – A case study in Shenzhen,
China
Lei YU1; Jian KANG2; Chen XING3
1,3

School of Architecture, Harbin Institute of Technology Shenzhen, China
2

School of Architecture, University of Sheffield, UK

ABSTRACT
Subjective evaluation of soundscape is much related with the sound sources. Due to ambiguous
understanding of annoy perception of a noise, a large number of soundscapes were recorded. The study
samples were obtained via intensive field works covering spatial and temporal variations of Shenzhen, China.
Using Co-kriging method, information content of sound source to all soundscape samples was thoroughly
analyzed. Forty-four typical sounds representing various sound sources in Shenzhen were gained.
Experimental research was conducted to depict how various sounds influencing subjective evaluations, inwhich more than two hundred participants were involved. Five soundscape categories were got based oncluster analysis on subjective annoyance evaluations to forty-four typical sounds. It is found that soundscapeswith more natural sounds were perceived less annoy whereas the ones having more mechanical sounds
perceived more annoy; however, to some soundscapes containing more urban living sounds or hybrid sounds,
subjective evaluations were not definite. A reason might be related with an individual’s social and cultural
background linking with former experiences from once happened incidents linking with social
activities/functions.
Keywords: Identification, Soundscape, Shenzhen

1. INTRODUCTION
Shenzhen is a coastal city located in the southern China near Hong Kong. Since having became theChina’s first special economic zone in 1979, it has transformed from an unknown area with many
fishing villages to one of the four China’s mega-cities nowadays. During such a transformation, thecity’s population has increase from less than 100,000 in 1979 to more than 20 million in 2017,
accompanied with huge traffic network built with a consequent of tremendous sound scape changes.
Although the city is quite modern currently, it remains some ecological characters in its development.
The city is reputed as ‘garden city’ in China with fruitful soundscapes, which need to be identified
due to increasing physiological and mental health problem brought by noise pollution resulting in
rapid industrialization and urbanization (1).
In EU, the Environmental Noise Directive (END) was promoted to assess and manage soundenvironment for creating quiet environments and reducing sound negative influence on community(2). The noise map was given to identify noise exposure based on sound level calculations. Despite
benefits of noise maps in controlling a high sound level area, a good sound environment does far notreach as sound sources have taken a part in deciding noise annoyance effects. Noise maps focus on
negative sound sources such as traffics and industrials, but do not give positive information forpleasant/preferred sounds such as water flowing, bird singing, or tree rustling sound (3). Although
being a metropolis, Shenzhen still has much mountainous and coast topography with ecologicalcomponents owning a garden city reputation. It is quite important to take sound sources into accountwhen managing urban sound environment, in which soundscape is more important.
A soundscape is defined as a perceived acoustic environment (4), psychological effects of asoundscape to people are emphasized. In order to create a healthy city with a good sound environment,
appropriate descriptor for a soundscape is crucial. Study pointed that affective quality of a soundscape1leilayu@hit.edu.cn
J.Kang@shef.ac.uk
3578448510@qq.com
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perception towards a 2-dimensional soundscape model is useful (5 . Among the many soundscapedescriptors, noise annoyance is acknowledged as a main indicator to evaluate noise and usually lead
immediate reactions of acoustic environment (6). Implementing soundscape approach to assess noise
annoyance has been broadly investigated. Former study suggested that except sound level, soundsource containing certain information is important to determine subjective noise perception (7). For
creating healthy environment with a regard of sound aspect, identifying a soundscape specially on its
annoyance attribute is necessary. Based on the soundscape concept, the study explored various
soundscapes in Shenzhen and provided feasible tool to identify a soundscape according to annoyancedescriptor from either sound level or sound sources.

2. METHEDOLOGY
For identifying a soundscape in Shenzhen, various acoustic environment samples have beencollected through intensive field works covering different urban forms of Shenzhen. As the samples
were gained according to the spatial disparity of the mega-city, they are assumed to present different
kinds of soundscape in Shenzhen. As sound level influence on a soundscape is definite and can beeasy measured, sound source (as another essential factor to soundscape) has been focused. In the study,
44 typical sound sources were abstracted from 702 samples employing Co-kriging analysis. The soundsources were used in lab experiments for giving annoyance evaluations. Following this, ANN models
were developed to present annoyance evaluation of sound sources based on acoustic andpsychoacoustic factors. Using such models, annoyance evaluation from certain sounds could bemeasured. Combining annoyance evaluation of sound source with sound level, a sou ndscape can be
identified with respect of annoyance descriptor by exclusively using acoustic and psychoacoustic
factors, which can be obtained through calculations.
2.1 Collecting study samples
Shenzhen is a dense city with a high population within a long east-west narrow strip area. It is thendeveloped along the east-west axis and formed various administration urban groups namely Da Peng,Long Gang, Bao An, Fu Tian, Nan Shan, and Luo Hu. Each one has its unique urban morphology that
configures its own soundscape features. The selected study sites for collecting soundscape samples
are selected to capture a wide range of urban configurations in terms of each administration urbangroup possibly expressing topological and morphological status of Shenzhen. Through a serial ofintensive field studies in Shenzhen, 702 soundscape samples are obtained by route measurements
meanwhile more than 60000 samples are collected by fixed measurements (8). The samples from routemeasurements reflect a diversity of Shenzhen soundscape according to spatial variations as 116
different study sites concluded, while the samples from fixed measurements represent a diversity ofsoundscapes due to temporal variations as measurements were made covering variabilities of season,
week, and day. In general, more than 60000 study samples reflecting various soundscapes in Shenzhen
are obtained. Geographically, soundscapes from the purely nature areas, low density built areas withsome natural components, and artificial areas barely with natural component are got. Temporally,various time’s soundscapes from different season, week, day, and time are also obtained.
2.2 Lab experiments
As sound source is the key determinant to a soundscape as containing information to affect peoples ’perception, lab experiments are designed to elicit subjective evaluations of different sounds. After
analyzing sound sources differentia to all collected samples based on spatial and temporalcharacteristics, 44 soundscape samples are selected from all the samples as assumed to stand for all
soundscapes of Shenzhen. The selection work is through firstly comparing soundscapes obtained in
the fixed measurements and the route measurements according to sound level variations and thenanalyzing amplitude domain, frequency domain, and time domain acoustic and psychoacoustic factorsof the collected samples (9). In order to get effective responses in the lab experiment, 44 samples fromfield studies were cut into 25 seconds recording. For excluding sound level influence on subjective
evaluation to sound source as sound meaning is emphasis, 44 recordings were adjusted to the same
sound level as 65 dB in the lab. The 44 sound samples and questionnaire surveys were combined using
MATLAB software and replayed randomly to the subjects. In total, 161 participants involved in thelab experiments with various social/demographic background.

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3. RESULTS
As sound levels and sources are key factors to decide a person’s perception of a soundscape,identifying a soundscape should take these two factors into account simultaneously. It is simple to geta value of sound level in measurement, however to get sound meanings only by measurement is rather
difficult. In the study, subjective evaluations of 44 recordings from the lab experiments used to know
affection information of sound sources. As noise annoyance is a key factor acknowledged as
evaluative aspects related to ‘noise’ (6), the study used noise annoyance indicator to categorize soundsources based on data obtained from lab experiments. Then, employing ANN models to predict soundsources according to the sound categories.
In order to use noise annoyance to categorize sound sources, hierarchical clustering analysis isapplied to quantitatively analyze relationship of sound sources to the noise annoyance evaluation. A
5-category from the least annoyance to the most annoyance is found to the 44 sound sources as shownin the Figure 1.

IV

III

II

V

I

Figure 1 – Hierarchical clustering analysis on 44 sound sources
To each sound source category, the exact involved sounds are shown in the Table 1, which can beseen that the Category Iincludes almost natural sounds such as sea waves, bird twittering, sounds of
streams, the Category II includes natural and also meaningful urban living sounds, the Category III
includes most urban living sounds but barely mechanical sounds, the Category IV includes somenatural sounds, some urban living sounds and some mechanical sounds, however the Category V
includes most mechanical sounds.
Table1 – Sounds and sound category according to the hierarchical clustering analysis
Sound category
,
,,
,,,
,9
9

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Avg. of Annoy Eva.
SoundsWave, wind, running water, rain, birdsong, insect
5.51
Birdsong, insect, human living, aircraft, music, rain, wind,
4.38
road traffic, yacht, running water, frog
Music, activity (playing ball), amplified broadcast, birdsong, insect,
3.83
road traffic, activity, human living, rain, machinery noises,
aircraft, wind
Birdsong, insect, human living, yacht, construction machine, human
3.35
living (commercial noise), wave, music, road traffic,
machinery noises
Running water, yacht, birdsong, insect, construction machine,
2.39
human living, machinery noises

According to the Table 1, it can be concluded that sound sources referring to annoy perception
could be quantitatively categorized and measured based on sound types included. The more naturalsounds the soundscape would be perceived less annoy, the more mechanical sounds the soun dscapewould be perceived more annoy. To the urban living sounds, their affection on annoyance are mostly
decided by how meaningful of it in a whole soundscape, when more natural sounds mixed with theurban living sounds, they turn to present active affection on mitigating annoy perception; but whenmore mechanical sounds mixed, they turn to show more negative affection. So it is acknowledged thatnatural sounds contribute good quality to a soundscape whereas mechanical sounds offer bad one
while urban living sounds are neutral.
Therefore, the following sections discuss how to identify a soundscape in Shenzhen according tosound levels and sources it contains. For calculating sound source contribution on a soundscape, adescriptor of occupation percentage of one kind of sound sources (natural, urban living, mechanical)
named PctN, PctU, and PctM, is used and obtained by listening the studied soundscape samples.
3.1 Soundscapes inside the ecological line
Shenzhen has a unique morphology according to an ecological line, which is appointed by theShenzhen Urban Planning and Natural Resources Bureau. The ecological line is defined according to
the hilly terrain and coastline topography that remains very natural areas inside the line whereas very
urbanization areas away from the line and hybrid areas mixed natural and artificial components aroundthe line. This provides interesting base to form a very different soundscape in this city.
To identify a soundscape inside the ecological line, study samples from 36 case study sites are used
including either route or fixed measurements. A variation sound level of soundscapes in these areas isfrom 35 dB(A) to 70 dB(A), and average sound level to all the time according to the fixed
measurements is 54.1 dB(A). However, the highest average sound level occurs in the afternoon(14:00pm – 17:59pm) and the lowest one occurs in the morning (8:00am – 10:59am). This variation
might be related with creatures’ activity time. Maybe with the same reason, the sound level in summer
is higher than that in winter although the difference is not obvious, but the variation in summer israther bigger than that in winter, indicating winter is the rather quiet season in the Shenzhen ecologicalarea.
Although there is very limit development inside the ecological line, there are still road constructions
and artificial green spaces and fish villages exist creating various fabric configurations in theecological areas60Table 2 shows the correlations of urban development indices with a soundscapereferring sound level and source parameters. It can be seen that only ratio of road areas and ratio ofgreen land areas have been involved which is because other indices are barely used as not much thatkinds of development made. From the Table 2, it can be seen that sound level and sound source
parameters are significantly correlated with ratio of road and green land; however, the correlation ispositive to positive to the road and negative to the green land referring sound level indicating more
green lands lower sound level and more road areas higher sound level. According to sound source, asignificant positive correlation is found to green land with PctN and road areas to PctM, whereas asignificant negative is found road areas to PctN and green land to PctM. The results show that moreroad areas would create more mechanical sounds (most traffic noise) and less natural sounds but more
green land would create more natural sounds both significantly.
Table2 – Relationships of Sound level and source with development indices inside ecological line
Ratio of road area
Ratio of green land area.225*-.255*
Sound Level-.557**.808**
PctN
.528**-.780**PctM
**. Correlation is significant at the 0.01 level*. Correlation is significant at the 0.05 level

3.2 Soundscapes outside the ecological line
For soundscapes outside the ecological line, 80 case study sites can be used while 20 of them fromthe complete urbanization areas and 60 from the areas mixed natural and artificial components. To thecomplete urbanization areas, sound level is varied from 48 dB(A) to 83 dB(A) and average is 64.7
dB(A). There is not much difference of sound level in different times of a day and also different
seasons of a year, which is unlike the areas inside the ecological line. To the hybrid areas surround2555

the ecological line, it is also found that barely difference of sound level in different times of a da y andalso not much difference in different season.
Although sound level changes in the areas outside the ecological line are limited, further study ofvarious urban development indices on a soundscape refereeing sound level and sources in theurbanization and hybrid areas has been made, results are shown in the Table 3 & 4. The Table 3 showsthat the sound level and PctN and PctM correlations with the development parameters in the complete
urbanization area. It can be seen that sound level is significantl y correlated with FAR, building
coverage density, ratio of road area, ratio of green land area. Except green land, all other correlations
are positive indicating with a denser development, a sound level is getting higher. However, thesituation is opposite to PctN although similar to PctM. All development indices have a negative
significant correlation with PctN except ratio of green land areas indicating a denser development
eliminating more natural sounds. To PctM, the number of building floors is not significantly correlated
with. Likely sound level, PctM has positive correlation with all the development indic es except ratioof green land area, indicating a denser development creating more mechanical sounds.
Table3 – Relationships of Sound level and source with development indices in the complete
urbanization areas
Building
Building
Ratio ofRatio of green
FAR
Land use
coverage density
floorsroad area
land area
0.091.328**.327**-0.03.333**-.281**
Sound Level-.300**-.364**-.439**-.239**-.173**.483**
PctN
.212**.137*.160**
0.091.257**-.306**PctM
**. Correlation is significant at the 0.01 level*. Correlation is significant at the 0.05 level

Table4 – Relationships of Sound level and source with development indices in the hybrid areas
Building
Building
Ratio ofRatio of green
Land use
FAR
coverage density
floorsroad area
land area
Sound Level.343**.396**.434**.190**.476**-.508**
PctN
-.604**-.484**-.546**-.332**-.472**.659**
PctM
.510**.334**.358**.304**.475**-.525****. Correlation is significant at the 0.01 level*. Correlation is significant at the 0.05 level3.3 Identifying a soundscape by using ANN
From above analyses it is found that identifying a soundscape needs to get knowledge of soundsources, which are obtained through calculating hearing times of natural sounds, mechanical sounds,
and urban living sounds to a soundscape recording in a certain time. However, such a way is notefficient for measure a soundscape in reality. Through lab experiments, 5 sound categories reflecting
subjects responding of annoyance evaluation can be got using the hierarchical clustering analysis. The
sound categories express information of sound sources as natural, mechanical, urban living meanings.
This can be seen from the Table 1 of sound category and its including sounds. It is thus prospectivethat sound sources of a soundscape could be measured if they could be predicted by measurement
parameters such as acoustic and psychoacoustic parameters. In the study, using artificial neuralnetwork (ANN) model to predict and judge sound source is developed to give a method to measuretype of sound sources containing in a soundscape.
For training ANN models to predict sound source for identify a soundscape, acoustic andpsychoacoustic parameters from amplitude domain, frequency domain, and time domain have been
considered as input, the output is the 5-category sound. As PctN, PctU and PctM are parameters to
describe sound sources closely related to the 5-category sounds, their relations with the acoustic andpsychoacoustic parameters are broadly analyzed as important in accuracy of ANN model prediction.
Table 5 shows correlations of the acoustic and psychoacoustic parameters with PctN, PctU and PctM
to the 5-category sounds.
Using the parameters significant to the sound source, two kinds of ANN models are developed:
one is the general model for all the 5 category sounds; the other is the sub models, one for the 3 of 5
category sounds and the one for the 2 of 5 category sounds as they are found having different
significant influence factors. The result shows that the sub model has better prediction with an average
accurate prediction of 0.83 to one and 0.73 to another whereas the general model did much worse with2556

an average accurate prediction of 0.53. This shows again that closer relationships between inputs andoutput are key determinant in which a general model is usually ineffective (10). To identify asoundscape according to sound level and sound source variations, the sound level should be firstly
considered critical factor in determining subjective annoy perceptions (10), however, when the soundlevel is up to a certain value (say 55 dB) and below to a certain value (say 65 dB), the meaning ofsound source might play more roles. And this can be measured by using ANN models.
Table5 – Relationships of sound sources with acoustic and psychoacoustic parameters
Sound
Fluctuation
Leq
L10
L50
L90
Loudness Roughness Sharpness
Tonalitysourcestrength
PctN -.322** -.309** -.325** -.330**
PctU .149
.15
.149
.135
,, PctN -.412** -.356** -.486** -.527**
PctM .331** .292** .415** .459**
PctU .174* .143* .175* .180**,,, PctN -.442** -.412** -.559** -.616**
PctM .174* .143 .269** .331**
PctU .457** .449** .517** .527**,9 PctN -.395** -.389** -.449** -.488**
PctM .189** .211** .173*.087
PctU .187** .163* .249** .359**
9 PctM .533** .530** .481** .445*
PctU .041 .015
.106
.223
,

-.287**.11-.469**.453**.106-.465**.203*.455**-.375**.119.231**.554**
-.059

-.094
.029
-.102
.242**-.149*
-.016
-.121
.169*
-.140*.326**-.158*
.444*
.068

-.156
-.023
.316**-.197**-.200**.453**-.375**-.231**
-.016
-.072
.077
.203
.015

.194
-.157
-.023
.139
.062
.119
-.170* -.182**
.117
.053
-.212** .215**.045-.288**.267**.027-.268** .148*-.222** -.067.431**
-.073
.451*
.073
-.295
.452*

**. Correlation is significant at the 0.01 level*. Correlation is significant at the 0.05 level

4. CONCLUSIONS
Using the soundscape samples from a wide range field studies in Shenzhen, the study explores
soundscapes in different areas of Shenzhen due to its ecological particularity. It is found that the soundlevel is usually low to the areas inside the Ecological Line with more natura l sounds and lessmechanical sounds whereas the sound level is usually higher to the complete urbanization areas withmore mechanical sounds. To identify a soundscape, sound level and source should be emphasized.
The more developed areas usually have high sound level and less natural sounds and more mechanicalsounds, but the less developed areas are opposite. Although a soundscape could be roughly identified
according to the urban morphological indices, specific identification based on quantitative descriptors
is still necessary in which measurement is required. It is easy to measure sound levels of a soundscapebut difficult to measure sound source. In the study, sound sources have been categorized into 5 basedon evaluation surveys in a lad condition. A good prediction model to sound sources is sub models. To
identify a soundscape affection on annoyance perception, sound level should be taken firstly and thensound source when the level falls in certain value.

ACKNOWLEDGEMENTS
The works presented in this paper are supported by the Shenzhen Habitat Environment Committee
and the Science and Technology Innovation Commission of Shenzhen Municipality. The authors
would like to thank for all research group members for hard works and useful communications.

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