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

The Synthesis of Soundscape using Genetic Algorithm and Popular
Songs
Mia SUHANEK1; Sanja GRUBEŠA1; Ivan DJUREK1; Antonio PETOŠIĆ1
1

University of Zagreb, Faculty of EE and Computing, Croatia

ABSTRACTThis paper proposes the concept of soundscape synthesis based on improving existing soundscapes with
optimization through Genetic Algorithm. As a goal function for optimization the parameter of somesoundscape called Total Distraction Coefficient (which depends on loudness, sharpness and loudness growthrate) is chosen by which one can synthesize an improved soundscape. To verify the proposed concept two
representative (and familiar to most urban residents) soundscapes are further analysed and compared (namely,a children's park and an expressway). Using Genetic Algorithm (with previously known Total Distraction
Coefficient) two new soundscapes are synthesized from each soundscape- one with higher and one with
optimum Total Distraction Coefficient (i.e. the soundscapes supposed to be perceived as "worse" and "better"
than the starting one, respectively). The obtained soundscapes can serve as a basis for further subjective
testing.
Keywords: Soundscape Synthesis, Genetic Algorithm, Total Distraction Coefficient, Subjective Perception

1. INTRODUCTION
Nowadays, each individual is exposed to noise on a daily basis. Noise pollution is often overlooked
when compared to other different environmental pollutions (e.g. air pollution, water pollution, soil
pollution etc.). However, same as the all aforementioned pollutions, noise exposure has anaccumulating character, meaning that the harmful effect of noise is detected only after a long period oftime (1). Long exposure to noise pollution can be displayed as a bad mood, fatigue, insomnia,
headache and loss of concentration, which causes reduced work ability and ultimately permanent
hearing impairment (2-4). Bearing in mind the ever increasing problem of noise pollution the conceptof soundscape was introduced in order to amend and supplement the assessment of noise and its effects
on humans (5, 6). Soundscape studies offer a more intuitive and interdisciplinary approach to noise
and its numerous consequences. This particular field of research is becoming very propulsive andeveryday more and more scientists with different areas of expertize are being included while trying to
make their contribution in terms of improving the overall quality of life especially in urban areas (e.g.
acoustic engineers, urban planners, psychologists, architects, doctors etc.). Their common goal is tolink objective and subjective acoustic parameters, to plan future urban infrastructures keeping in mind
the existing soundscapes (e.g. building while not deteriorating the sound environments) and to create
and implement new solutions that will design, preserve and improve acoustic environments (7, 8).
Thus, we can conclude that nowadays more and more soundscape studies are oriented towards humanhealth, well-being and overall quality of life (9).
Bearing in mind all of the aforementioned, in our previous research we have proposed a simple
acoustical model, which allows one to quantify how much typical urban acoustic environments are
subjectively perceived in terms of affecting the listeners’ concentration. For that purpose, we have1

mia.suhanek@fer.hrsanja.grubesa@fer.hr
1ivan.djurek@fer.hr
1antonio.petosic@fer.hr
1884

recorded several different soundscapes which were then reproduced to listeners in laboratory
conditions. It was found that the linear model is adequate for calculating the parameter which we
named respectively Total Distraction Coefficient (TDC). For instance, the lowest TDC was obtained
for the soundscape of stream (10).
The motivation for this research is to use the Genetic Algorithm (GA) as an instrument which will
optimize the parameter Total Distraction Coefficient (TDC) and furthermore allow us to synthesize
improved versions of existing soundscapes. The obtained soundscapes were then tested and comparedin terms of subjective perception.

2. EXPERIMENT: THE TOTAL DISTRACTION COEFFICIENT (TDC)
OPTIMIZATIONIn our previous research (10) we have obtained a mathematical expression for the numericalparameter which we term Total Distraction Coefficient (TDC):(1)ܶ‫ ܥܦ‬ൌ ͲǤͳ ή ‫ܮ‬஽ூி ൅ ͲǤͲʹ ή ‫ܦܩܮ‬஽ூி ൅ ͲǤͶͶ ή ܵ஽ூிwhere L DIF, LGDDIF and SDIF are ratios of difference value and the mean value of the three consideredobjective acoustic parameters themselves namely loudness, loudness growth, and sharpness (e.g.
loudness difference was calculated LDIF=ΔN5 /N mean). This numerical value is envisaged as acharacteristic value of the soundscape and can serve as an objective parameter or a measure for
evaluation of soundscapes. Linear regression and linear model have been selected after calculating
both linear and nonlinear models. It was found that nonlinear models do not give significantly better
dependence on the results of the three aforementioned acoustic parameters. This claim is verified by
the fact that the standard error was significantly higher in nonlinear models. Thus, linear model can beconsidered as adequate enough for determination of the distraction coefficient.
For this particular study we have selected two soundscapes which are familiar to most urban
residents: a children’s park and an expressway (shown in Figure 1). The calculated devi ation factors(objective measures) and Total Distraction Coefficient (TDC) for the considered soundscapes areshown in Table 1.

Figure 1 – The selected locations for further analysis
Table 1 – The calculated deviation factors and Total Distraction Coefficient for the consideredsoundscapesSoundscape type

LDIF

LGDDIF

SDIF

TDC

Children's park

1.48

3.9

0.27 0.343

Expressway

0.83

3.45

0.28

0.28

In addition, our previous studies have shown that the soundscape of a stream has the lowest Total

885

Distraction Coefficient TDC = 0.184 (10). Therefore, this experiment is designed in order to achieve
the same TDC for the children’s park and expressway by using the Genetic Algorithm (GA).
2.1

The Genetic Algorithm (GA)The Genetic algorithm (GA) is an adaptive empirical search algorithm based on the evolutionary
ideas of natural selection and genetics (11). As such it represents an intelligent exploitation of arandom search used to solve optimization problems. Although randomized, GA is by no means random,
instead they exploit information to direct the search into the region of better performance within thecertain search area. The basic techniques of the GA are designed in order to emulate processes innatural systems necessary for evolution, especially those which follow the principles of Charles
Darwin i.e. “survival of the fittest”.
Genetic algorithm starts with an initial set of random solutions called population and eachindividual in the population is called candidate units.
The basic structure of the Genetic Algorithm (shown in Figure 2) is:
1. Setting a random initial population P that is made from x i candidate units, where i =1, 2, … ,N;
2. Determining the Fitness Value of all candidate units from the population P;
3. Selecting the best candidate units from the population P according to the predefined Fitness
Value (the number of best candidate units which enter a new iteration of GA can be definedaccording to the study hypothesis);
4. Creating a new population i.e. best candidate units and new units created by using genetic
operators:
a. Crossover P2 = Crossover (P1)b. Mutation P2' = Mutation (P1');
5. Determining the Fitness Value of all candidate units in a new population P2;
6. Returning to step 3 until the break condition is achieved.

Figure 2 – Genetic Algorithm flowchart
In this particular study, we have defined for Genetic Algorithm the TDC (Fitness Value) to be
exactly 0.18 (the obtained TDC from our previous research for the soundscape characterized as themost pleasant and calming one i.e. stream). The size of population is ten and in each iteration the GA
preserves the two best candidate units while generating eight new ones. In order to create new
candidate units according to Eq.1 we needed to change the ranges of following parameters: theloudness difference range from 0.5 to 1.5, the loudness growth difference range from 3 to 6 and the886sharpness difference range from 0.1 to 0.3. The ranges for all three acoustic parameters are changed
with a step of 0.05. The mentioned ranges and step were chosen based on our findings in previous
research on a larger number of acoustic environments.
2.2

The optimized solutions for calculating Total Distraction Coefficient

After performing fifty iterations, the Genetic Algorithm has provided nine possible optimized
solutions for calculating Total Distraction Coefficient (shown in Table 2). We have decided to further
investigate the second solution (marked yellow in Table 2) which provided the largest alteration
primarily for the loudness ratio value (L DIF) and in addition for the ratio of loudness growth difference
value (LGDDIF).
Table 2 – Optimized solutions for calculating Total Distraction Coefficient

ID

LDIF

LGDDIF

SDIF

1

0.55

4.25

0.10

2

0.80

3.00

0.10

3

0.50

3.40

0.15

4

0.75

3.25

0.10

5

0.50

4.50

0.10

6

0.55

3.15

0.15

7

0.60

4.00

0.10

8

0.65

3.75

0.10

9

0.70

3.50

0.10

3. SYNTHESIS OF NEW SOUNDSCAPES
In order to achieve the optimized Total Distraction Coefficient for the aforementioned soundscapes
we have tried to use acoustical methods such as compression and filtering of higher frequencies.
However, in our opinion this is very unpractical in real case scenarios i.e. performing audio
compression and filtering of higher frequencies in in-situ conditions. Therefore, we have decided touse different popular songs in the process of new soundscapes’ synthesis.
3.1 The selection of songs
The selection of used songs was not done completely randomly. At this stage of research we havedecided to use some of the “classics” and on the other hand songs that are extremely popular i.e. songs
that are often played on radio stations. Furthermore, we chose two genres in particular rock and pop
songs and for each genre a song which is perceived as happy or optimistic and a song perceived as sador depressing. The selection of songs is shown in Table 3.

887

Table 3 – Selection of songs

CLASSICSHAPPY/OPTIMISTIC

SAD/DEPRESSINGROCK Queen “Bohemian Rhapsody”
POP

Guns N’ Roses “November Rain”

John Lennon “Imagine”

Jeff Buckley “Hallelujah”
MODERNPOP

HAPPY/OPTIMISTIC

SAD/DEPRESSING

Beyoncé “Crazy in Love”

Miley Cyrus “Nothing Breaks like a Heart”

In addition, for each individual song prior to mixing it with soundscapes we have calculated Total
Distraction Coefficient (shown in Table 4.). The mean sound pressure level used for calculation of
loudness parameters and TDC was 50 dB(A).
Table 4 – Total Distraction Coefficient for the chosen songs

SONG

LDIF

LGDDIF

SDIF

TDC

Queen “Bohemian Rhapsody”

0.9

3.2

0.33

0.30

Guns N’ Roses “November Rain”

0.47

2.1

0.18

0.17

John Lennon “Imagine”

0.5

1.82

0.38

0.25

Jeff Buckley “Hallelujah”

1.2

3.5

1.02

0.64

Beyoncé “Crazy in Love”

0.37

1.79

0.24

0.18

Miley Cyrus “Nothing Breaks like a Heart” 0.41

3.1

0.21

0.20

Finally, in order to proceed with the experiment we have eliminated the extremes (two lowest andtwo highest TDCs) and the mixing was done with two songs marked yellow in Table 4 (i.e. John
Lennon “Imagine” – song 1 and Miley Cyrus “Nothing Breaks like a Heart” – song 2).
3.2 The process of mixing the selected soundscapes with chosen songs
The process of mixing soundscapes with the two chosen songs was done in the following way. Eachsoundscape was mixed with three versions of each song (shown in Table 5.). The 100% of a song’s
mean level corresponds to a 100% of soundscape’s mean level in the steady part of the soundscape(equivalent continuous sound pressure level corresponds to 50 dB(A)). The 50 seconds long examples
of soundscape signals mixed with songs signal versus time are shown in Figures 3 and 4. Figures 3aand 4a show loudness of the soundscape and the mixed signal, while figures 3b and 4b shownormalised level of soundscapes and songs signals. If we analyse the results shown in Table 5. It can beconcluded that by raising the percentage of a song in the mixed signal, the Total Distraction
Coefficient becomes closer to the calculated Total Distraction Coefficient of a song. If we go back to
the results obtained from Genetic Algorithm in terms of the desired TDC (i.e. Fitness Value 0.18) we
can observe that the best result was achieved for the combination of childr en's park and Miley Cyrus
song “Nothing breaks like a heart” with a 100% loudness. In order to proceed with the experiment, wehave eliminated the extremes (two lowest and two highest TDCs) and the mixing was done with twosongs marked yellow in Table 4.

888

Figure 3 – An example of mixing: children’s park with 100% Miley Cyrus “Nothing Breaks like a
Heart”; a) loudness of mixed signals versus time, b) normalised level versus time of each signal

Figure 4 – An example of mixing: expressway with 100% John Lennon “Imagine”; a) loudness of
mixed signals versus time, b) normalised level versus time of each signal
Table 5 – Total Distraction Coefficient for the soundscapes mixed with chosen songs

Ldif

LGDdif

Sdif

TDC

Ldif LGDdif

CHILDREN'S PARK

1.5

3.9

0.3

0.36 EXPRESSWAY 0.98

+Lennon 40%

1.3

3.8

0.29 0.33+Lennon 40%+Lennon 80%

0.88

3.8

0.29 0.29+Lennon 80%+Lennon 100%

0.67

3.5

+Miley 40%

1.24+Miley 80%+Miley100%

Sdif

TDC

3.8

0.28 0.30

13.8

0.28 0.30

0.86

2.86

0.3

0.31 0.27+Lennon 100% 0.74

2.1

0.29 0.24

4.3

0.24 0.32+Miley 40%

0.97

3.9

0.25 0.29

0.6

3.7

0.18 0.21+Miley 80%

0.73

2.6

0.23 0.23

0.42

2.93

0.17 0.18+Miley 100%

0.52

2.55

0.23 0.20

0.28

4. CONCLUSIONSThis paper deals with the concept of soundscape synthesis based on improving the existingsoundscapes with optimization of Total Distraction Coefficient calculations using the Genetic
Algorithm. Total Distraction Coefficient depends on loudness, sharpness and loudness growth rate.

889

Our previous studies have shown that the soundscape of a stream has the lowest Total Distraction
Coefficient TDC = 0.184, therefore, this experiment is envisaged in order to achieve the same TDC for
the children’s park and expressway. In our experiments we have obtained the desired TDC for the
aforementioned soundscapes using acoustical tools such as compression and filtering of higher
frequencies. However, in our opinion these methods are extremely unpractical to apply in real casescenarios in the field of soundscape. When speaking about indoor soundscapes, filtering of higher
frequencies can actually be achieved using specific acoustic absorptive materials and collaboratingwith architects regarding the layout of e.g. open-space offices.
For outdoor soundscape our idea was to test different songs in terms of calculating their TDC.
Regarding the choice of songs, we have decided to use some of the “classics” songs together with thesongs that are often played on radio stations. After calculations we have decided to eliminate theextremes and use two songs i.e. John Lennon’s “Imagine” (TDC = 0.2536) and Miley Cyrus’s“Nothing Breaks like a Heart” (TDC = 0.1954). Each soundscape was mixed with three versions ofeach song (40%, 80% and 100% of song’s level) where a 100% of a song’s mean level corresponds toa 100% of soundscape’s mean level in a steady part of a soundscape (i.e. 50 dB(A)). The tests haveshown that by increasing the percentage of a song’s level the overall TDC becomes closer to the TDC
of a song.
The best result with respect to the desired TDC was obtained for the case of children’s park mixed
with “100% loud” Miley Cyrus’s song “Nothing Breaks like a Heart”.
In order to confirm our findings, the next stage of research will be focused on conducting extensive
listening tests on urban residents. Furthermore, the obtained formula for calculating the Total
Distraction Coefficient will need to be expanded and a correction factor which takes into account thehuman perception will be applied. In addition, we plan to extend the base of songs used forsynthesizing the soundscapes.

ACKNOWLEDGEMENTS
This work has been supported by the European Union from European regional development fund
(ERDF) under the project number under the project number KK.01.2.1.01.0103 Acoustical Camera (inCroatian: Akustička kamera).

REFERENCES
1. Goines L, Hagler L. Noise Pollution: A Modern Plague. Southern Medical Journal; 2007; 100(3); p.
287-294.
2. Ising H, Kruppa B. Health effects caused by noise: Evidence in the literature from the past 25 years.
Journal Noise and Health; 2004; 6(22); p. 5-13.
3. Basner M, Babisch W, Davis A, et al. Auditory and non-auditory effects of noise on health. Lancet
PMC. 2014; 383(9925); p. 1325-1332.
4. European Commission. Noise impacts on health. Science for Environment Policy; 2015.
5. Schulte-Fortkamp B, Fiebig A. Soundscape Analysis in a Residential Area: An Evaluation of Noise andPeople’s Mind. Acta Acustica united with Acustica; 2006; p. 875-880.
6. Brambilla G, Maffei L. Responses to Noise in Urban Parks and in Rural Quiet Areas. Acta Acustica
united with Acustica; 2006; p. 881-886.
7. Brown L. A Review of Progress in Soundscapes and an Approach to Soundscape Planning.
International Journal of Acoustics and Vibrations; 2012; p. 73-81.
8. Aletta F, Kang J, Axelsson O. Soundscape descriptors and a conceptual framework for developing
predictive soundscape models. Landscape and Urban Planning; 2016; p. 65–74.
9. 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; 2018; p. 1-15.
10. Suhanek M, Djurek I, Grubeša S, Petošić A. Urban Acoustic Environments – An Acoustic Model for
Total Distraction Coefficient. Acta Acustica United with Acustica; 2019; p. 334-342.
11. Davis L. Handbook of genetic algorithms. Van Nostrand Reinhold, New York. 1991.

890