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

Classifying urban public spaces according to their soundscapeKang SUN1; Karlo FILIPAN2; Francesco ALETTA3; Timothy VAN RENTERGHEM1;
Toon DE PESSEMIER1; Wout JOSEPH1; Dick BOTTELDOOREN1; Bert DE COENSEL1,2,*
1

WAVES Research Group, Department of Information Technology, Ghent University / imec, Belgium
2

ASAsense CVBA, Bruges, Belgium
3

University College London, UK

ABSTRACT
Cities are composed of many types of outdoor spaces, each with their distinct soundscape. Some of thesesoundscapes can be extraordinary, others are often less memorable. However, most locations in a city are notvisited with the purpose of experiencing the soundscape. Consequently, the soundscape will not necessarily
attract attention. Existing methods based on the circumplex model of affect classify soundscapes according to
the pleasure and arousal they evoke, but do not fully take into account the goals and expectations of thelistener. Therefore, in earlier work, a top-level hierarchical classification method was developed, which
distinguishes between spaces based on the degree to which the soundscape creates awareness of theacoustical environment, matches expectations and arouses the listener. This paper presents the results of an
immersive laboratory experiment, designed to validate this classification method. The experiment involved
40 participants and 50 audiovisual recordings drawn from the Urban Soundscapes of the World database. It isshown that the proposed classification method results in clearly distinct classes, and that membership to theseclasses can be explained well by physical parameters, extracted from the acoustical environment as well as
the visual scene.
Keywords: Soundscape, Classification, Urban space

1. INTRODUCTION
The soundscape of urban outdoor public spaces contributes to the perceived quality of those spaces,and eventually to the identity of a city or a community. Over the past decades, awareness of thetransience and the heritage value of these soundscapes has risen, and more and more efforts are spent
to record and catalog urban soundscapes for posterity. A well-known early example is the World
Soundscape Project initiated by R. Murray Schafer (1), which has led to a set of recordings called theWorld Soundscape Library. Technological progress is providing a helping hand in speeding up thisprocess. More recent examples are the use of mobile applications and citizen science to collect urban
soundcape recordings (2, 3), or several projects that aim to collect high-quality immersive audiovisual
recordings at urban locations worldwide, such as the Urban Soundscapes of the World project (4). In
this light, soundscape data collection methods have become a subject of standardization efforts within
the ISO 12913 series of standards (5).
Data collection often goes hand in hand with data classification, and this is also the case for urban
soundscapes. Several approaches to classify urban spaces according to their soundscape have been
proposed in the past. These classification methods can be based either solely on acoustical properties,
such as in (6), or on a combination of acoustical, visual and other perceptual properties, such as in (7).
Holistic methods, which assess the soundscape as a whole, are often inspired by the circumplex modelof affect (8), and classify soundscapes according to the pleasure and arousal they evoke (9). However,
the application of the core affect model assumes that one actually pays attention to the soundscape.
While this might be the case by design in most laboratory experiments, most often, city dwellers do nothave the explicit purpose of experiencing the soundscape. Moreover, the acoustic environment inmany urban locations does not necessarily attract attention if sound is not the purpose of being there.
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bert.decoensel@ugent.beTaking into account the goals and expectations of the listener may lead to a more nuanced
classification. In (4), a hierarchical approach is proposed, to categorize urban locations concerning
how the soundscape at these locations is typically perceived. In a first stage, locations are classified
according to the degree to which the soundscape influences the perception of the total environment.
The scale varies from no influence at all (meaning that one does not pay attention to the soundscape) toa strong influence (meaning that the soundscape immediately attracts attention and creates awareness).
In a second stage, locations are scaled according to how well the soundscape matches expectations.
These expectations can be both place-related (e.g. congruence with the visual surroundings) andperson-related (e.g. how much the soundscape interferes with the goals and activities of the listener).
The scale ranges from disruptive to supportive, and relates to the pleasantness axis in the core affectmodel (9). Finally, in a third stage, locations are scaled according to how they arouse the listener. The
scale ranges from calming to stimulating, and relates to the arousal axis in the core affect model.
In (10), an operational method (in the form of a small questionnaire) is presented, which allows
assessing audiovisual recordings according to the hierarchical classification approach proposed above.
In Sections 2 and 3, this paper briefly presents the results of an immersive laboratory experiment,
designed to validate this operational method. Results of this experiment have been published inextended form in (11). In Section 4, a discussion of these results is presented.

2. METHODOLOGY
2.1 Audiovisual stimuli
The audiovisual stimuli used in the laboratory experiment are drawn from the Urban Soundscapesof the World database (4). This database consists of immersive audiovisual recordings, i.e. combined,
simultaneous spatial audio, and 360-degree video, collected in a range of cities worldwide. In each city,
recording locations were selected in a systematic manner, through an online survey among 30 to 50
inhabitants (depending on the city). Participants in this survey were asked to pinpoint those outdoor
public spaces within their city that they perceive as either being full of life and exciting, chaotic andrestless, calm and tranquil, or lifeless and boring. These adjectives were taken from the four quadrantsof the principal component analysis (PCA) performed in (9). A spatial clustering analysis allowed
compiling a shortlist of prototypical locations with a variety of soundscapes, more or less uniformly
covering each of the four quadrants in the two-dimensional core affect perceptual space. Moreinformation about the site selection protocol can be found in (4). For the present experiment, 50
one-minute stimuli (including first-order ambisonics spatial audio track) were extracted from thedatabase, recorded at different locations within the cities of Montreal, Boston, Tianjin, Hong Kong andBerlin. We refer to (11) for a complete list of all stimuli.
From the recordings, a range of acoustical and non-acoustical indicators was calculated. Acousticalindicators included the one-minute LAeq of the stimuli, which ranged from 53.3 dB(A) (recorded inTiergarten, Berlin) to 77.0 dB(A) (recorded in Peking Road, Hong Kong), as well as percentile levels,
loudness, sharpness and auditory saliency (12). Non-acoustical indicators included the people density(qualitatively labeled on a 5-point scale) and the amount of green elements (by proxy of the percentage
of green pixels) in the 360-degree scene. Figure 1 illustrates the method for identifying green elements,on the basis of the image extracted from the opening scene of the 360-degree video.

Figure 1 – Illustration of the calculation of green coverage; this example contains 20.6% green elements.
Considering all 50 recordings, 22% had no people at all in them, 30% had a small number of people,
26% an average number, 14% a high number, and 8% a very high number. The percentage of green
elements ranged from 0% to about 60%. Overall only a very small negative correlation (r2 = 0.104)
between LAeq and amount of green elements is observed.

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2.2 Experiment setup
During the experiment, participants were seated inside a soundproof booth, with the experimenter
monitoring the experiment from outside. Audiovisual stimuli were played back using a PC placedoutside the booth. The 360-degree video was presented through an Oculus Rift head-mounted display;
participants could freely move their head and look around. The audio was played back through a pairof Sennheiser HD650 headphones, driven by a HEAD acoustics LabP2 headphone amplifier calibrated
to ensure that the audio is played back at the original sound level. To have a smooth run of theexperiment, questions were projected within the head-mounted display, and participants were asked toanswer verbally; the experimenter would then mark down the answers. This way, the participants did
not need to take the head-mounted display off all the time during the experiment.
2.3 Participants
In total, 40 participants (29 male, 11 female, mean age 29.5 yr, range 22-46 yr) took part in theexperiment, recruited mostly among students at Ghent University. All had normal hearing, assessed
via pure tone audiometry, and normal color vision, tested by the Ishihara test for color deficiency (13).
Participants were offered a gift voucher as compensation for taking part in the experiment.
2.4 Experiment outline
The participants performed the experiment individually. At the start of the experiment, eachparticipant was briefly informed about the experimental procedure. Subsequently, the 360-degree
videos with first-order ambisonics audio track were presented in random order. To keep the total
duration of the experiment within bounds, participants were split into two groups of 20 people, eachbeing presented with only half of the stimuli. After each audiovisual stimulus, participants were asked
two sets of questions. In the first set of questions, participants had to rate the locations they had
experienced, on an 11-point scale in terms of the adjectives used for selecting the recording locations,as mentioned in Section 2.1. The second set of questions consisted of those proposed in (10) for thehierarchical classification approach, and are listed in Table 1. Note that either question 5a or 5b isasked, depending on the answer on question 1. At the end of the experiment, a small questionnaire wasadministered, containing questions of demographic nature.
Table 1 – List of questions and possible answers for the hierarchical soundscape classification method.
1. In general, how would you categorize the environment you just experienced? [5-point scale from
“calming/tranquil” to “lively/active”]
2. In general, what kind of activities could you imagine doing in this environment? [list of activities]
3. How much did the sound draw your attention? [5-point scale from “not at all” to “extremely”]
4. Would the sound environment prevent you from doing the activities mentioned above? [5-point
scale from “not at all” to “extremely”]
5a. How much does the sound environment contribute to the calmness/tranquility of this place?
[5-point scale from “not at all” to “extremely”]
5b. How much does the sound environment contribute to the liveliness/activeness of this place?
[5-point scale from “not at all” to “extremely”]

3. RESULTS
3.1 Principal component analysis
The first set of questions leads to average scores for each of the 50 soundscapes considered, on thefour core affect model classes “full of life/exciting”, “chaotic/restless”, “calm/tranquil”, and“lifeless/boring”. In order to obtain a similar score for the four classes “backgrounded”, “disruptive”,“calming” and “stimulating” that are considered in our proposed hierarchical classification method, afuzzy set membership analysis is applied to the answers on the second set of questions. Answering “notat all” or “a little” on Question 3 (Table 1) leads to a score of 1.0 for the “backgrounded” class. The
other possible answers on the 5-point scale gradually lead to lower scores; the answer “extremely”
leads to 0.0. Answering “highly” or “extremely” on Question 4 leads to a score of 1.0 for the“disruptive” class, and in a similar way scores for “calming” and “stimulating” are obtained from theanswers on questions 5a and 5b. This procedure is applied to each soundscape/participant combination,and for each soundscape, the average membership over all participants for each class is calculated.

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Subsequently, two principal component analyses (PCA) are performed on the average scores: once
based on the scores for the four core affect model classes, and once based on the scores for the fourclasses in the alternative hierarchical classification method. Figure 2 shows scatterplots of allsoundscapes along the two principal components, for each of the two PCA analyses.
2

3

(a)

1

2

0

1

‐1

0

‐2

‐1

‐3

(b)

‐2
‐3

‐2
‐1
none
exciting
chaotic+exciting
chaotic+tranquil

012chaotic
tranquil
exciting+tranquil

3

‐3

‐2

‐1

none
disruptive
stimulating
disruptive+stimulating

0

1

2

3

backgroundedcalming
backgrounded+calming

Figure 2 – PCA component plots for (a) the core affect model, and (b) the hierarchical classification method.
For the PCA based on the core affect model scores (Figure 2a), component 1 explains 55.1% ofvariance, while component 2 explains 30.9%. For the PCA based on the hierarchical classification
method scores (Figure 2b), component 1 explains 71.1% of variance, while component 2 explains
22.1%. Thus, a slightly higher total variance is explained by the hierarchical classification method.
Individual soundscapes in Figure 2 are colored according to the class with the highest score, or labeled“none” if there is no clear class that has the highest score for the given soundscape.
3.2 Models based on acoustical and visual factors
To analyze the contribution of underlying acoustical and visual factors to the classification of eachlocation, a generalized linear mixed model (GLMM) is constructed for the four proposed classes, using
a stepwise procedure, with participant as random factor. Table 2 shows the fittest model for each of theclasses of soundscapes, based on the Akaike Information Criterion (AIC). Acoustical factors include
sound level (L), loudness (N), sharpness (S) and saliency (SL), together with associated extreme andpercentile levels. Visual factors include greenness (G) and person density (P), as a categorical value,
dummy coded where P1 means no people at all, up to P4 , meaning a high amount of people.
Table 2 – Generalized linear mixed models for the classes of the hierarchical classification method.
Soundscape class
Backgrounded

AIC
319.2

Disruptive

511.1

Calming

591.1

Stimulating

535.7

Membership
0.458 0.041 ∙ 𝐿
0.023 ∙ 𝑁
0.068 ∙ 𝑆
0.037 ∙ 𝑆𝐿
0.116 ∙ 𝐺
0.547 ∙ 𝐿
0.035 ∙ 𝑆𝐿
0.480 ∙ 𝑆
1.432 0.525 ∙ 𝐿
0.040 ∙ 𝑁
0.046 ∙ 𝑁 0.302 ∙ 𝑆
0.145 ∙ 𝑆
1.327 0.020 ∙ 𝐿
0.172 ∙ 𝑃
0.024 ∙ 𝑃
0.003 ∙ 𝑃
0.057 ∙ 𝑃 0.106 ∙ 𝑆
0.755 0.196 ∙ 𝑃
0.077 ∙ 𝑃
0.064 ∙ 𝑃
0.091 ∙ 𝑃 0.067 ∙ 𝑆𝐿

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3.3 Models solely based on acoustical factors
In order to predict the membership of a given soundscape to each of the four classes within thehierarchical classification approach, a set of linear regression models was constructed, exclusively
based on the acoustical parameters extracted from the spatial audio recordings. Table 3 shows theresulting linear regression models.
Table 3 – Linear regression models for the classes of the hierarchical classification method.
Soundscape class
Backgrounded
Disruptive
Calming
Stimulating

Membership
0.018 ∙ 𝐿
1.464
0.027 ∙ 𝐿
0.015 ∙ 𝐿
0.733
0.020 ∙ 𝐿
0.079 ∙ 𝑆
1.440
0.078 ∙ 𝑆𝐿
0.643

r2
0.521
0.488
0.426
0.501

sig.
0.000
0.006
0.098
0.000

4. DISCUSSIONThe GLMM model results presented in Table 2 show that visible green elements reduce the chance
for a soundscape to become labelled as backgrounded. Moreover, the model for the backgroundedclass has the lowest AIC value. This suggests that, based on acoustical and visual factors, it would be
relatively easy to predict if a soundscape will draw attention or not. The model for predicting if asoundscape will be backgrounded solely based on acoustical factors, as shown in Table 3, only retains
LA05 as an acoustical indicator. Thus, to be backgrounded, soundscapes should simply not contain any
loud sounds, whatever their origin or duration.
A disruptive soundscape prevents the users of a space from performing the activities that theywould otherwise engage in. The optimal generalized linear mixed model for the disruptive classcombines many non-orthogonal acoustical factors but contains no visual factors, which might indicatethat sound dominates perception in a disruptive soundscape. The predictive model for the disruptiveclass in Table 3 contains both LA05 and LA05 –LA95 with a positive coefficient, indicating that the soundlevel as well as the temporal variability of the sound are determinant for the soundscape to become
disruptive.
Supportive soundscapes, either calming or stimulating, are expected to contribute to the overall
experience of a place, matching expectations created by the context and purpose of the place. The
results of Table 2 indicate that calming or stimulating support is for a large part evoked by visual
information. Although the amount of visual vegetation is not a significant factor for explaining why
soundscapes are calming or stimulating, however, the visual presence of people plays a key role: too
many people present reduces the calmness of the soundscape. Furthermore, sharpness (S 50) and theabsence of strong peaks in the sound level (LAFmax ) appear in both the GLMM and linear regression
models for the calming soundscapes. Sharpness is typically higher for natural sounds (which arecommonly perceived as calming) and lower for mechanical sounds (which are commonly perceived as
non-calming). As expected, the number of people is found to be positively correlated with thestimulating character of a soundscape. Finally, auditory saliency (SL), a property of the sound thatcharacterizes its ability to draw attention, and that focuses strongly on vocalizations (12), appears inboth the explorative GLMM and predictive models for the stimulating soundscapes. This suggests that(bottom-up) auditory saliency might be indicative in explaining the stimulating character of asoundscape, whereas voluntary (top-down) auditory attention might be indicative in explaining thecalming or tranquil character of a soundscape (14).

5. CONCLUSIONSThis paper presented the results of a laboratory experiment, designed to validate a hierarchicalapproach for classifying urban outdoor public spaces according to their soundscape. This approach, forwhich an operational assessment method was proposed in earlier work, is seen as an alternative to themore conventional way of classifying soundscapes based on the circumplex model of core affect. The
experiment involved the perceptual evaluation of immersive audiovisual recordings drawn from the
Urban Soundscapes of the World database. The proposed hierarchical classification method wasshown to result in distinct classes, and membership to these classes could be explained well byparameters extracted from the acoustical environment (spatial audio) as well as the visual scene.

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ACKNOWLEDGEMENTS
This study was supported by the HEAD Genuit Foundation through grant agreement No. P-16/11-W,“Urban Soundscapes of the World”, and was partially carried out in the framework of the C3PLACES
project, which received funding from the European Union’s Horizon 2020 research innovation
programme under grant agreement No. 693443.

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