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

Evaluating Shenzhen Sound Environment by Using Artificial Neural
Networks&+(1 ;,1*1; /(, <82;-,$1 .$1*3;=+,;,$1* 7$24;
1,2,4

Harbin Institute of Technology Shenzhen, China
3

University College London, 8.

ABSTRACT
Attenuating absolute sound level for urban noise control is not always efficient in which sound meanings has
to be included. As evolving from fish-villages with a coastal rural topography, Shenzhen possesses a diversity
sound environment containing various ecological sounds. In order to get right knowledge for controlling
Shenzhen sound environments, sound meanings have to be measured to give a complete profile. Through aseries field studies in Shenzhen, 702 samples covering various sound environments were got. Statisticalanalyses of heterogeneity of sound environments in Shenzhen were firstly examined. Based on results ofsubject evaluation of sound level present in a former study, Shenzhen sound environment referring soundlevels were investigated. Furthermore, annoyance evaluations to various sounds with a same level 65dB weremade using field study data. In order to measure sound meaning attributes influencing sound environment
quality, plenty ANN models were developed to predict annoyance evaluations according to sound meaning
differences. Finally, combining the results of sound levels and sound meaning, predicting models to a soundenvironment in Shenzhen were given to provide a feasible tool in measuring and solving Shenzhen noiseproblems.
Keywords: Subjective evaluation, sound environment, Shenzhen, Artificial Neural Network (ANN)

1. INTRODUCTION
With a constant speeding up urbanization, the city scale of Shenzhen is largely expanding
accompanying a series of environmental problems. Amongst them, noise becomes increasingly
prominent. According to the bulletins on environmental situation from the Human Settlements andEnvironment Commission of Shenzhen municipality, the noise complaints reached the first rank in all
kinds of environmental complaints and the number is still going up. However, it is not always efficient
to subjective noise attenuation just monitoring and controlling sound levels. Plenty authority works
have proven that sound levels and sound meanings are main attributes in determining a soundenvironment quality (1, 2). With a regard of subjective facet, a sound environment could be describedby absolute and relative sensations of a person responding to sounds and the occurring environment
under listening conditions (3). Therefore, absolute level reduction to certain sound sources may notcause high quality sound environment, because the character of the sound is equally important. Somesounds have a positive impact, whereas others have a negative regardless of their sound levels (4).
Schafer defined ‘soundscape’ as a proper term to note a sound environment with emphasis on the way
it is perceived (5). Ever since the concept emerged, researchers have wondered how soundenvironments would affect peoples’ life in a city. Although sound environment is a physicalphenomenon, considering its affective effects on human lives, soundscape approach that takes
information of a sound environment the same important is more used (6, 7). To measure a soundenvironment quality, subjective evaluation of various sounds should be taken into account. Most
importantly, judging a sound environment should be through human perception. However, it has beenless reflected when coping with a sound environmental problems presently in China. It is very
1578448510@qq.com
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incorrect specially in some ecological city such as Shenzhen. According to the report of the Shenzhen
Environmental Monitoring Station, sound levels often surpass 60dB in many ecological areas insummer time due to bird singing (8). Hence, it is necessary to consider sound sources difference whenmonitoring a sound environment in Shenzhen for getting right knowledge to improve society wellbeings.
Although previous studies have acknowledged the importance of taking sound sources informulating a sound environment quality, quantitative studies of differentiating sound sources are still
less, resulting in confusing understanding when measuring sound environment in the environmental
monitoring works. This paper addresses a method to evaluate a sound environment regarding soundlevels as well as sound sources. Since having successfully predicted subjective evaluations ofsoundscape and sound preference and sound sources (9,10,11,12,13), ANN models developed to
predict sound sources according to subjective evaluations. The study is conducted as giving a practical
tool in solving noise monitoring problems brought up by the Shenzhen Environmental Monitoring
Station.

2. METHODOLOGY
In order to evaluate quality of various sound environments in Sh enzhen, intensive field studies
have been carried out ranging very different built environments (14). Based on 702 sound environment
samples recorded from field studies, statistical analyses of sound levels to different soundenvironments have been made. Then, 44 typical sound sources were extracted standing for 702 soundenvironments. Following this, 25 seconds recording of 44 typical sources were cut used in laboratory
experiment to elicit annoyance evaluation due to sound source variation. According to results fromthe laboratory experiment, ANN models were developed to give annoyance evaluation uniquely fromsound source differences. The hypothesis of the study is the successful ANN model can measure
annoyance of sound source as complimentary in the current monitoring work of sound environment.
Data analysis
2.1
Through field studies, 702 samples representing all different kinds of sound environments inShenzhen were obtained. Firstly, sound level differences were investigated to all collected soundenvironment samples. In general, the samples were recorded in Shenzhen six administration districts,
named the District I, II, III, IV, V, VI. From the District I to VI, the study site contains less and lessnatural elements whereas more and more artificial works. In Figure 1, it shows the sound level changes
in one day of the six administration districts. Based on in situ observation, Districts I is the placeremained many natural components, e.g. mountain topography, natural villages with a rather longcoastline. Among the six administration districts, it has the lowest urban development. The District II
is the residential, commercial and industrial area, and it has middle development stage with lower
construction intensity. The District III is constituted by residential area and industry with low intensityof development. Districts IV is residential, commercial and financial services area with higher
development intensity and small natural, and has the higher density of the road network. Districts V
is constituted by industry and coastline of the longest region. Districts VI is center of city, constitutedby living, commercial office, school, hospital and other service facilities area with the highest
development intensity and larger artificial green area. In the Figure 1, it can be found that theadministration districts I has the lowest Leq and the highest Leq in districts VI, with a difference ofnearly 20dB. Districts II, III, IV have similar Leq, especially districts V and VI. Analyzing the built
environment, it is understood that administration districts I has the lowest construction and the largest
green area. The construction of districts VI is the most extensive. Among of their Leq (From Districts
I to VI), the area with the largest natural distribution has the lowest sound level and the quietest
environment. In the meanwhile, the area with more artificial components is found to have higher
sound level.

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Figure 1– Sound level changes in one day of six administrative districts
To all the 702 sound environment samples, many sound sources included, however, three main
kinds of sound types can be classified as many studies pointed (7). They are natural sound sources,
urban living sound sources (basically from man’s living and activities), and mechanical sound sources.
In order to explore relationships between various sounds and their incident environment, soundenvironment samples were also concluded from three kinds of environments, namely natural soundenvironment, mix sound environment, and artificial sound environment. Among them, natural soundenvironments are mainly distributed in the mountain topography areas of District I, III, IV, V, VI. In
the same time, it has less mechanical sound. The artificial sound environments are mainly distributed
in artificially built environment areas which far away from the natural environment of mountains,parks and water. These areas have obvious sound sources of urban living and machinery. In addition,
the mix sound environments are distributed in mountains and water topography areas, especially themountains of Districts II. While the natural sound sources are obvious, the sound sources of urban
living and machinery still exist. Then, in order to quantitatively describe three kinds of sound sourcesin their incident corresponding environment, the acoustic and psychoacoustic parameters of 702 soundenvironment samples were calculated.
2.2 Laboratory experiments
In order to explore how a sound environment to influence a community only with a regard of soundmeaning differences, laboratory experiment in order to elicit annoyance evaluations of the different
sound sources with a certain sound level (say 65 dB) was constructed . In all, 44 typical sound sourceswere extracted from 702 sound environment samples by comparing acoustic and psychoacoustic
parameters, which can represent typical sound environment in Shenzhen. In order to get correct
responds from subjects, the replaying time in the laboratory is controlled to be 25 seconds that is longenough to give aural perception to a sound environment. At the same time, The L eq of each signal was
fixed to 65 dBA by adjusting the amplitude of the signal and the volume of the amplifier. The
experiment lasted about 45 minutes, with a 1-minute break in the middle. Experiment place is locatedin the listening room, and the background noise is below 40 dB.
In order to the subject evaluation of sound source, the content of the questionnaire mainly includes
two parts: personal background information and subjective perception of sound source. The subjective
evaluation is divided into 1-9 levels (Indicators of quietness, comfort, loudness, annoyance andpleasure). The lower number show the bad evaluation, and the higher number show the goodevaluation. In total 328 people were interviewed. According to the 44 sound source representative
samples, a total of 14,432 questionnaires were collected. Alternatively, the study deleted 11
questionnaires with errors. Finally, 14,421 valid questionnaires were obtained.
2.3 Categorizing sound source
According to subjective evaluations, the 44 laboratory study sounds can be assembled into 5-scale
category by using cluster analysis. The result is shown in Figure 2. It can be seen that according tosubjective evaluation of the quietness only according to sound source difference 5 categories could823

be obtained, the quietness evaluation category from 1 to 5: Category 1 is very quiet, which is mainly
the natural sound type, including wave, water, bird and insect sound and their mixed sound. Category
2 is quiet, which is mainly the mixed sound with obvious natural sound and the clear man’s living andactivities sound. The evaluation of this kind of sound source is also relatively high, mainly between
4-5. Category 3 is general, which is mainly composed of mixed sound with more living sound.
Although the natural sound is involved, its influence is weak. The evaluation of this kind of soundsource is mainly between 3.5-4. Category 4 is noise, which is mostly mixed sound and commercial
noise involving mechanical sound. Influenced by the mechanical sound such as automobile andaircraft, it has a low annoyance evaluation, and its value is mainly between 3.1 and 3.5. Category 5 isvery noisy, which is mainly mechanical sound and the mechanical sound. Thus, the subjective
annoyance evaluation results of the sound source be obtained, and in order to identify the category of
the sound source, 5 categories were selected to train ANN models.

4

3

2

5

1

Figure 2–Category of subject of evaluation
2.4 ANN modeling
In order to evaluate a sound environment regarding sound sources difference, ANN models were
employed to give quantitative results of annoy affect from sound source. This is because annoyanceevaluation from sound source facet is not able to be measured. In the study, a 5-scale category ofsound source was used to show annoy affection from very quiet to very noisy.
As a close relationship is quite important to ANN models’ prediction (13), relationships of various
factors from acoustic and psychoacoustic parameters to subjective annoyance evaluation of a soundwere explored, in which person correlation was used. According to the relationship of subjective
evaluation and factors from sound amplitude domain (Leq, L10 , L90, loudness(L)), frequency
domain(roughness(R), sharpness(S) and tonality (Ton)), and time domain (fluctuation strength (Fls))
have been calculated by Artemis. Totally, 8 significant correlation factors were chosen to be input in
ANN models. In the meanwhile, 5-sound source categories got from section 2.3 were assigned as theoutputs in the ANN model.
Figure 3. shows detailed information for the networks, including input variables and network
structures. The number of input layer nodes of each model is equal to the number of input
characteristics, the output layer node number is 5-sound source categories. The activation function ofeach hidden layer is linear rectifier function (Relu), used to increase the nonlinear neural network, theactivation function of output layer uses the softmax function, is used to predict the probability of eachcategory, loss function was Cross Entropy, optimization algorithm was root back propagation
optimization algorithm(RMSProp), each of the models is iterated 1000 times . Finally, the best
artificial neural network is used to predict the evaluation of sound source in Shenzhen.

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Figure 3 –Structure of ANN model

3 RESULTS
It is well understanding that sound level is key factor to affect annoyance perception of a soundenvironment. However, sound sources also take an important role to a person’s calm assessment tocertain sounds when the sound level is not very high (say 65dB). Using sound environment samples
from field studies, the study explored annoyance evaluations of various sound environments inShenzhen in order to give a correct understanding of noise annoyance in such an ecological city. Soundlevel and sound sources are two aspects key determinants in a sound environment that have beenstudied respectively. To annoyance evaluation from sound level facet, an evaluation model was
proposed based on reviewed previous studies. To annoyance evaluation from sound source facet, ANN
models was employed based on categorized sound sources according to subjective evaluations inlaboratory experiments.
3.1 Evaluating sound environment in terms of sound levels.
Literature reviews showed that sound level directly affects subjective evaluations of a soundenvironment. Study has been done to subject’s noisy responding to sound level used ANN model
prediction (16). A former study pointed that although sound level is the key factor to determine noisy
evaluations, factors from social/demographic aspects have also some influence regarding sound level
differences (17).

Figure 4 –Sound level evaluation model regarding subjective facets

Based on results of previous studies (16-17), annoyance evaluation of a sound environment
regarding sound level was studied, a result of annoyance evaluation to sound level was given in the
Figure 4. It shows that the subjective annoyance evaluation deteriorates as the sound level increases.
However, the subjective annoyance evaluation is similar in a range of sound level. Thus, theannoyance evaluation can be graded into 3 ranks, from rank I to III. The sound level of rank I is below
55 dB. The subjective annoyance evaluation is relatively quiet. Rank II is between 55 dB and 65 dB

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and subjective evaluation in general. Rank III is greater than 65 dB, sound evaluation is accounted fornoisy. Then, in order to get results of the sound level annoyance evaluation in various soundenvironment, it can be directly judged according to the sound level.
Based on sound level evaluation model as shown in Figure 4, annoyance evaluation of sound levelto the Shenzhen was studied. Annoyance evaluation of sound level to different sound environment
was analyzed as shown in Figure 5. It can be seen that the natural sound environment is mainly
constituted of very quiet and general, the proportion of noisy is relatively small, so the sound level isbasically below 65 dB, and a large part is below 55 dB. The mixed sound environment is general. It
is indicated that the sound level is mainly between 55 dB and 65 dB. The arti ficial sound environment
has a rating below general evaluation, especially noisy evaluation, while very quiet evaluation is very
less, indicating that the sound level is basically 55 dB, and mostly above 65 dB. Therefore, from theevaluation results of the three type of sound environment, the natural sound environment is thequietest, the mixed sound environment in general, and the artificial sound environment is the noisiest.
100%
80%
60%
40%
20%
0%

Natural sound Mixed soundenvironment
environment
Very Quiet
GeneralArtificial soundenvironment
Noisy

Figure 5 – Difference in sound level evaluation between sound environment
3.2 Prediction sound source by ANN
Besides sound level, sound source is another key factor in evaluating a sound environment
referring to annoying perception (6). However, it is difficult to measure annoyance evaluation of asound source due to involving plenty factors from acoustic and non-acoustic characters. Therefore,
laboratory experiment was conducted to get annoyance evaluation of a sound source using datacollected from field studies. Finally, sound sources that representing various sound environment inShenzhen were clustered into a 5-scale categories according subjective evaluation. In building an
ANN model, the 5-scale categories of sound sources were used as outputs. And, all possible output
variables which including acoustic and psychoacoustic parameters to subjective annoyance evaluationof a sound were used as inputs, e.g. L eq , L10 , L90, loudness(L), roughness(R), sharpness(S), tonality(Ton)), and fluctuation strength (Fls). The highest accuracy results of prediction showed that the ANN
model which has category 1-3 and 4-5 output variables are the best. And, in the best ANN model, thenumber of hidden layers is 2, the nodes of the first layer are the twice the number of inputs, and thenodes of the second layer is 32.
Table 1 –Comparison of different input variables in ANN

Model
1
2
3
4
5

Output

Input

Layer
Note Iterations Maximum Average
accuracy accuracy
number (1/2Layer)
Leq, L10, L50, L90, L, R, S and Fls
2
16/32
1000
0.86
0.73
Category Leq, L10, L50, L90, L, Ton, S and Fls
2
16/32
1000
0.80
0.69
(1, 2, 3)
Leq, L10, L50, L90, L, S and Fls
2
14/32
1000
0.85
0.72
18/32
1000
0.90
0.80
Category Leq, L10, L50, L90, L, R, S, Fls , Ton 2(45) Leq, L10, L50, L90, L, Ton, S and R
2
16/32
1000
0.90
0.83

The study compared prediction results for different input characteristics in the grouping ANN
models. By comparing prediction results in Table 1, it can be seen the average accuracy of the model
1,2,3 is basically above 0.7, and the maximum accuracy is above 0.8. Meanwhile, the average accuracy826

of the model 4 and 5 is above 0.8, and the highest accuracy is 0.9. It is indicated that this kind ofgrouping model can better predict the sound source evaluation. Model 3 has the best results, with anaverage accuracy of 0.73, a maximum accuracy of 0.89. Therefore, in ANN, using the input variablesof L eq , L 10 , L50, L90 , loudness(L), roughness(R), sharpness(S) and fluctuation strength (Fls) of model
3 can accurately predict sound sources of category 1, 2 and 3. Comparing model 4 and 5, it can befound that Model 5 has the best prediction results, with an average accuracy of 0.83, a maximum
accuracy of 0.9. Thus, input variables of L eq, L10 , L 50, L90, loudness (Ton), roughness(R), fluctuation
strength (Fls) and tonality (Ton) of Model 5 can make an accurate prediction to sound sources toCategory 4 and 5 sound sources.
3.3 Evaluating sound environment in terms of sound sources
According to the ANN model, this paper predicted the subjective annoyance evaluation of soundsource in Shenzhen. Annoyance evaluation of sound sources consists of very quiet, quiet, general,
noisy, and very noisy, which is also categorized into 1-5. Through laboratory experiment, Figure 6
presents annoyance evaluation of sound sources to Shenzhen various sound environments.
100%
80%
60%
40%
20%
0%

Natural sound Mixed sound Artificial soundenvironment environment environment
Very Quiet Quiet General Noisy Very Noisy 
Figure 6 – Difference in sound source evaluation between sound environment

It is found that the natural sound environment is obviously evaluated as the best with more than
50% very quiet and quiet evaluation. The mixed sound environment evaluated as in general with morethan 40% very quiet and quiet evaluation, and 40% very noisy and noisy evaluation. Howeverˈtheartificial sound environment is obviously evaluated as the worst with 0% very quiet , and more than
60% general, noisy and very noisy evaluation. In general, the subjective annoyance evaluation ofsound source in the natural sound environment is the quietest, the mixed sound environment in general,and the artificial sound environment is the noisiest. Although the results gave annoyance evaluationof sound source to a sound environment, quantitative study to underpin annoy ance evaluation of acertain sound is important in measuring a sound environment regarding sound source. This has been
given by using ANN model prediction as can be seen in the section 3.2.

4 CONCLUSIONSIn order to evaluate sound environment in Shenzhen, 44 typical sound sources were extracted
from 702 sound environment samples collected through intensive field studies. The studied samples
are supposed to stand for all possible sound environment in Shenzhen as already covering spatial andtemporal variation of Shenzhen. After analyzing all study samples, three types of sound environments
can be classified, namely natural sound environment, mix sound environment, and artificial soundenvironment. As the sound level and sound sources are key factors to assess annoyance evaluation ofa sound environment, they were focused in this study. To sound level, an evaluation model was
proposed based on reviewed previous studies, it found although sound level is constantl y varied,annoyance evaluation referring to sound level is quite concentrated that can be clustered to very quietˈ
general and noisy. In Shenzhen, the natural sound environment is found to be quietest, the mixed
sound environment is general, and the artificial sound environment is the noisiest. To sound source,
the laboratory experiment was conducted to get annoyance evaluation. A 5 -scale categories wereobtained according to analyze subjective evaluations of sound sources with a certain level (65 dB) . It
found likely evaluations of sound level, annoyance evaluation of sound source in the natural soundenvironment is also the quietest, the mixed sound environment is general, and the artificial sound827

environment is the noisiest. Unlike sound level, it is difficult to measure annoyance perception
according to sound source variation. Therefore, ANN models have been employed to give quantitative
results of a sound environment with respect of sound source difference . The result shows that ANN
models could give relatively accurate prediction of annoyance evaluation if the grouping model withcertain sound source categories were used.

ACKNOWLEDGEMENTS
Research works in this paper are funded by the Shenzhen Science and Technology Innovation
Commission of Shenzhen Municipality. The authors acknowledge their financial support and useful
discussions with experts in urban environmental and planning areas.

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