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

Annoyance modeling of construction noise using acousticalfeatures, noise sensitivity and health condition
Jae Kwan LEE1; Seo Il CHANG1; Soo Il LEE1; Jae Woong JANG1
1

University of Seoul, Korea, Republic of

ABSTRACTThe construction site noise is the biggest cause of the noise dispute in Korea. A listening
experiment was conducted to produce annoyance prediction models for four kinds of construction
noise that cause the most complaints. These noise were generated by the construction machinery
such as the pile driver, the excavator, the pumping vehicle, and the concrete mold removal working.
These four kinds of noise recorded at the construction site were adjusted to the stimuli with 35 ~ 80
dB(A), and they were played to the subjects and the annoyance score was surveyed. The objective
indicators of the stimuli and the survey results of subjects were used as variables to develop theannoyance models caused by construction noise. The annoyance models were developed by multiple
regression. Acoustic features include traditional indicators such as Leq, Lmax, as well as indicatorsof spectral features and temporal features proposed in recent studies. And sound quality indices wereused to model. These were calculated mean, maximum, and percentiles values of loudness,
sharpness, Roughness and fluctuation strength. Also noise sensitivity and health condition were usedas the variables of annoyance prediction models. These were obtained from subjective questionnaire.
Keywords: Construction noise, annoyance, multiple regression, subjective variables

1. INTRODUCTION
Construction noise, which is a mixture of noise generated by various construction machines andworks, is generating the most complaints in Korea. In this study, we evaluated the annoyance ofconstruction site noise and investigated the factors affecting the annoyance. There are many
previous studies evaluating the annoyanc. “Especially, the method of estimating the degree ofannoyance using the% HA curve is useful (1, 2).” The %HA curve predicts annoyance using only
noise level, but there are also cases where the annoyance is predicted using other variables. “In caseof predicting annoyance using sound quality, explanatory power (R2 ) was 0.856(3).” “There are alsostudies using variables other than the acoustic characteristics of noise stimuli such as gender,
experiment time (4).” In this study, we also developed a model to evaluate and predict annoyanceusing various variables as well as noise level.
Experiments were conducted to evaluate the annoyance of construction noise recorded at theconstruction site. Two experiments were conducted and the first is an experiment to evaluate theannoyance of single construction machine noise. In the second experiment, combined noise wasproduced similar to the actual construction site and the subjects evaluated the annoyance.
“‘When the noise level is more than 65dBA, the annoyance of complex construction noise ishigher than that of single noise.’ is reported by Hong JY (5).”After the experiment, models was developed to predict the annoyance of construction noise byusing the acoustic characteristics of the stimulus used in the experiments (objective indicator) andthe pre-survey result (subjective indicator). Two types of models were developed using multiple
linear regression. The variables of first annoyance model are the acoustical indicators of the noise
stimuli used in experiment. In the second model, objective and subjective indicators are used as
variables. The subjective variables are the gender, age, and health status of the subjects answered by
the participants. After completing the modeling, the explanatory power of each model and therelative importance of the variables were calculated.
1

jklee645@gmail.com

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2. EXPERIMENT METHOD
2.1 Recording
The construction noise for the experiments was recorded at the actual construction site. Fourkinds of construction noises were recorded in Seoul and the suburbs. The work noise of the
excavator and the pile driver used for the civil engineering work, the noise of the concrete pump carused for the construction work, and the noise generated when removing the concrete form wererecorded. The noises of pump car and pile driver are continuous sound, the excavator noise and theconcrete form falling noise are impulsive sound.
Two-channel microphones (SQuardriga1, BHS1) in the form of headphones were used forbinaural recording. Each noise was recorded at around 30 seconds so that no noise other than thetarget sound was recorded.
2.2 Stimuli for experiment
Since two experiments were conducted, stimuli for each experiment were prepared. The first is anexperiment to evaluate annoyance of single noise, and the second is an experiment to evaluate theannoyance of combined noise. In order to use recorded noise for two experiments, four original
sounds recorded at the construction site were cut into 10 seconds. Noise stimuli cut into 10 seconds
were converted into various noise levels to survey annoyance rate according to level changes.
In Experiment 1, the level of the noise stimulus used in the experiment is 35 ~ 80dBA, and thedifference in level between each noise stimulus is 5dBA. The number of stimuli for experiment 1 is40.
In Experiment 2, two construction noises were mixed for annoyance rating. Two kinds of
combined noises were fabricated using single noise stimuli used in Experiment 1. Of the stimuli in
Experiment 1, only 50, 60, and 70 dBA were used in the synthesis. Combined noise was produced toinclude both continuous noise and impulsive noise. The first is a combination of civil engineering
noise (pile driver and excavator) and the second is a combination of building construction noise(pumping vehicle and concrete mold falling). The figure 1 below is an example of a combined
stimulus. The number of stimuli for second experiment is 12.
G

Figure 1 – Example of combined stimuli
2.3 Environment of experiment
The experiment was performed in the auditory test laboratory. The auditory test laboratory
consists of a control room and a test room. The background noise level of the test room is around
30dBA. In addition, temperature and humidity environment was kept constant, temperature was
DERXWႏKXPLGLW\ZDVDERXW When the experimenter reproduces the stimulus in the control
room, the subject were stimulated in the test room. The subject can hear the noise with a speaker orheadphones. In this study, headphones (Sennheiser HD 660s) were used.


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Figure 2 – The test room in auditory test laboratory
2.4 Annoyance rating
The experiment was divided into two experiments depending on the type of noise stimulus. It is asingle noise experiment that can only hear one construction noise and a combined noise experimentthat tells the noise of a combination of two construction noises. In Experiment 1, the subject listened
to single construction noise at a time. Subjects in Experiment 2 listened to combined construction
noise. Both experiments were conducted to evaluate the annoyance of the subjects after giving themnoise stimuli. The subjects participated in the experiment were 60 persons in the single noise
experiment and 48 persons in the combined noise experiment.
One of the 40 noise stimuli prepared for the first experiment was randomly reproduced to thesubjects and the annoyance score was evaluated using the 11 point scale. In the same way, theannoyance questionnaire for the remaining stimuli was conducted.
In the second experiment, one of the 12 stimuli was randomly presented. The annoyancequestionnaire was conducted using the 11 point scale same as in Experiment 1. In the same way, theannoyance questionnaire for the remaining 11 stimuli was performed.

3. RESULTS
3.1 Pre-survey result
Prior to the annoyance questionnaire, subjects were surveyed in advance. In the preliminary
questionnaire, the subjects' gender, age, level of education, health condition of the subjects werequestioned. In addition, four self-questions related to noise were performed. Self-questions are'noise sensitivity', 'usual noise exposure level', 'experience of discomfort due to noise', 'experienceof sleep disturbance due to noise'.
60 subjects (male 20, female 40) participating in the first experiment have various ages ranging
from 10 to 70 years. Of the respondents, 15% said they had a disease and 22% said they were very
sensitive to noise. Also, among the subjects more than 20 years old, the proportion of subjects
whose education level is higher than that of the university is 68%.
46 subjects (male 22, female 24) participating in the second experiment had an age distribution
ranging from 10 to 60 years. The proportion of subjects with the disease is 26%, slightly higher than
the first experiment. The ratio of subjects who are very sensitive to noise is 20%, similar to the ratioin Experiment 1. The percentage of highly educated people is 77%.
3.2 Analysis of stimuli used experiment
The acoustic characteristics of the noise stimuli presented to the subjects were analyzed. The
analysis items are general noise indicators such as equivalent noise level and maximum noise level,
and five psychoacoustic indexes. Loudness and sharpness were analyzed as mean values(‫ܮ‬௠௘௔௡ , ܵ௠௘௔௡ ) of stimulus. Roughness and fluctuation strength were analyzed as mean(ܴ௠௘௔௡ , ‫ܨ‬௠௘௔௡ ), maximum (ܴ௠௔௫ , ‫ܨ‬௠௔௫ ), and percentile values (ܴଵ଴ , ܴହ଴ , ܴଽ଴ , … ).
In addition, “the total energy of tonal components within critical bands from 12 to 24 Barks(TETC) was used (6).” “While the loudness and sharpness are calculated using specific loudness of
all frequency bands from 0 to 24 Bark, the TETC is calculated by integrating tonal component

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values of frequency bands from 12 to 24 Barks (6, 7).” These acoustic and psychoacoustic indicators
were calculated using Head Acoustics Artemis and statistical package R. Acoustic indicators used
for analysis is table 1.
ଶସ ୆ୟ୰୩
଴

Shrapness = 0.11

(1)

N ᇱ dzLoudness = නଶସ‫׬‬଴ N ᇱ g(z) dz

(2)

ଶସ

ଶସ

‫׬‬ଵଶ NԢ dz

TETC = 10݈‫݃݋‬ଵ଴(න 10௅(௭)/ଵ଴ dz)

(3)

ଵଶ

Table 1 – Acoustical indicators used in analysis
Acoustic indicator
Equivalent noise level‫ܮ‬௘௤

Maximum noise levle‫ܮ‬௠௔௫

Loudness‫ܮ‬௠௘௔௡

Sharpnessܵ௠௘௔௡Roughnessܴ௠௘௔௡ , ܴ௠௔௫ , ܴଵ଴ , ܴହ଴ , ܴଽ଴Fluctuation Strength‫ܨ‬௠௘௔௡ , ‫ܨ‬௠௔௫ , ‫ܨ‬ଵ଴ , ‫ܨ‬ହ଴ , ‫ܨ‬ଽ଴

Total energy of the tonal components

TETC

3.3 Annoyance model
The annoyance prediction model was developed based on the subject's annoyance score, acoustic
indicators, and pre-survey results. Multiple regression analysis was used to construct the model andthe final model was optimized through the variable selection process. In the initial model with all
variables, only the necessary variables were optimized through the backward elimination process.
The annoyance model was developed in two types. The first type model is an objective variable
model. The model using the annoyance rating results and the acoustic indicators of the stimuli wasproduced for each experiment (MO1, M O2 ). For the second model, both objective and subjective
variables were used. Unlike the first model, in which only the acoustic indicators of noise stimuli
were used as independent variables, the results of the questionnaire survey were added to the second
model (MOS1, MOS2 ). The variables added to the second type are the results of eight preliminary
surveys.
For the MO1 model using the acoustic index of the first stimulus, six variables were selected after
the variable selection process and the explanatory power (ܴ ଶ) was 0.7867. In the case of the MO2model using the second experimental results, three variables were selected in the model
improvement process and the explanatory power (ܴ ଶ) was 0.4757. The categorical variable, Source,
was selected only on the MO1 model, and it was confirmed that the annoyance score was higher
when there were file driver noises compared to other types of noise.
In the M OS1 model, not only the variables selected in the M O1 model but also seven subjective
variables were additionally selected. Correlation coefficients show that men are sensitive to noise,and those who have experience of being disturbed by their daily life due to noise are interpreted as
more annoying to noise. On the other hand, it was interpreted that subjects with experience of sleep
disturbance had lower annoyance. In the case of the MOS2 model using the results of Experiment 2,
five subjective variables were additionally selected. Unlike the MOS1 model, subjective variables forgender and noise sensitivity were removed. The explanatory power (ܴ ଶ ) of the M OS2 model is0.6029, which is higher than that of the MO2 model. Table 2 is the annoyance models of experiment
1 and 2.

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Table 2 – Annoyance models of experiment 1 and experiment 2
Experiment 1

Experiment 2

MO1

MOS1

MO2

MOS2

Variable

Coef.

Coef.

Variable

Coef.

Coef.

Intercept-4.2187-5.7642Intercept-11.7010-13.6693‫ܮ‬௘௤

0.2767

0.2766

Continuous ‫ܮ‬௘௤

0.12400.1240

Loudness-0.1347-0.1346

Impulsive ‫ܮ‬௘௤

0.15790.1579Sharpness

5.87455.8683‫ܨ‬୫ୟ୶

7.1593

7.1593

ܴ୫ୟ୶

0.68530.6853Age

0.3879TETC-1.3963-1.3939

Disturb(Yes)-0.3862

Source(EXC)

1.78291.7806

Education(M)0.3755

Source(PILE)

4.55674.5563

Education(H)-0.9422

Source(PPV)2.87502.8753

Education(U)0.7240Gender(M)0.2519

Education(G)0.4585Age

0.0931

Usual noise

0.1522

Disturb(Yes)

0.4379Sleep(yes)0.5228

Education(M)0.8096

Education(H)0.3838

Education(U)0.4540

Education(G)0.5221

Sensitivity

0.0670Usual noise

0.0332Sleep(yes)-0.2011ܴଶ
Adjusted ܴ

0.7867
ଶ

0.78570.80180.8002ܴଶ
Adjusted ܴଶ0.47570.60290.47190.5948

3.4 Variables weighting
The relative importance of selected variables for each model was evaluated. “The relative
weighting of the variables can be evaluated by making all possible sub-models in the main modeland by calculating how much the ܴ ଶ value increases on the average when one explanatory variable
is added (8).” The calculated relative weighting for each variable are shown in Table 2. The noiselevel (‫ܮ‬௘௤ , dBA) of the stimulus used in the experiment was the most important variable in bothmodels (M O1 , M OS1). The second most important variable was R max, followed by Loudness, TETC,
and Sharpness. The significance of the variables in the MOS1 model using the subjective factors aswell as the acoustic characteristics was similar to MO1 . The ratio of the subjective variables to the
explanatory power of the model was 1.74%.
The noise level of impulsive noise was the most important variable in MO2 and M OS2 using thesecond experimental result. Among the subjective variables, the most influential is the age, and thehigher the age, the higher the annoyance level. In the MOS2 model, the ratio of the subjective
variables to the explanatory power of the model is 18%, which is higher than that of the MOS1
model. This is interpreted to be due to the fact that the number of acoustic indicators selected for
M OS2 is three, which is small compared to the model of Experiment 1.

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Table 3 – Weight of variables each model
Weight (%)

Weight (%)

Variable

MO1

MOS1

VariableG

MO2

MOS2

‫ܮ‬௘௤

22.04

21.66

Continuous ‫ܮ‬௘௤

38.83

31.81

Loudness

19.24

18.91

Impulsive ‫ܮ‬௘௤

45.16

36.99

Sharpness

15.03

14.75

‫ܨ‬୫ୟ୶

16.01

13.12

ܴ୫ୟ୶

21.90

21.52

Age

13.25

TETC

15.03

18.25

Disturb

0.41

Source3.23

3.17

Education

1.78

Gender

0.15

Usual noise

2.21

Age

0.41

Sleep

0.41

Disturb

0.46

Education

0.06

Sensitivity

0.36

Usual noise

0.27

Sleep

0.03

4. CONCLUSION
Four kinds of noise from the construction site were recorded and two experiments wereconducted. In the first experiment, the subject listened to one kind of noise stimulus and thenevaluated the annoyance. In the second experiment, he listened to the combined noise stimulus andthen evaluated the annoyance. Models using objective variables and models using objective andsubjective variables were developed using the annoyance results of each experiment. Four models
are developed, MO1 and MO2 are objective variable model, and MOS1 and M OS2 are subjective
variable model.
The explanatory power of MO1 and MO2 are 0.79 and 0.48, respectively. The explanatory power of
M OS1, MOS2 was 0.80 and 0.60, which is higher than MO1 and MO2. The explanatory power of MOS1
increased by 0.015, while the ܴ ଶ of M OS2 increased by 0.13.Commonly selected subjective variables
after variable selection are age, education level, sleep disturbance experience due to noise. However,there is a case where the correlation coefficient between the performance score and the score is
opposite.
The relative importance of each variable in the explanatory power of the model was evaluated. In
common, the most important variable was the magnitude of the stimulus (noise level). The
importance of subjective variables was 1.74% in MOS1 and 18% in MOS2, which was relatively low
compared to the acoustical indicators. Since the explanatory power of the model is not significantly
improved and the ratio of the subjective variables to the explanatory power is also low, it is judgedthat the subjective variables have little influence on the evaluation of the annoyance of the single
noise. In MOS2, the ratio of the subjective variables to the explanatory power is higher than that of
M OS1, but the explanatory power of the model itself is low. Acoustic indicators and subjective
variables were statistically significant when evaluating the annoyance from construction noise, but
the relative importance of subjective variables is lower than acoustic indicators.

ACKNOWLEDGEMENTS
This research was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01060891)

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REFERENCES
1.
Finegold L.S, Harris C.S, von Gierke H.E, Community annoyance and sleep disturbance:
updated criteria for assessing the impacts of general transportation noise on people. Noise Control
Engineering Journal, 1994; 42(1). p. 25-30.
2.
Changwoo L, assessment method of adverse effects on health from environmental noise.
[Seoul]: Seoul national University; 2006 Feb. Korea
3.
Ellermeier W. Predicting annoyance judgements from psychoacoustics metrics: Identifiableversus neutralized sounds. Proc Inter-noise 2004. August 22-25. Prague Czech Republic 2004.
4.
Kuhnt S, Schürmann C, Schütte M, Wenning E, Griefahn B, Vormann M, Hellbrück J.
Modelling Annoyance from Combined Traffic Noises: An Experimental Study. ACTA ACUSTICA
UNITED WITH ACUSTICA. 2008; 94. p. 393̽400.
5.
Hong JY, Lee SC, Jeon HY. Relationship between combined construction noise andannoyance. Proc KSNVE annual spring conference 2015; April 2015. Jeju, Korea 2015. p. 389-391.
6.
Trollé A, Marquis-Favre C, Klein A. Short-term annoyance due to tramway noise:determination of an acoustical indicator of annoyance via multilevel regression analysis. ACTA
ACUSTICA UNITED WITH ACUSTICA. 2014; 100, p.34-45
7.
Fastl H, Zwicker E. Psychoacoustics: facts and models. 3rd edition. Springer. Berlin,
Germany; 2006
8.
Robert I. Kabacoff. R in action. Manning. Shelter island NY, USA; 20117687