Towards acoustic indicators for soundscapedesign

Francesco AlettaSchool of Architecture, University of Sheffield, Sheffield, United Kingdom.

Östen AxelssonSchool of Architecture, University of Sheffield, Sheffield, United Kingdom.Department of Psychology, Stockholm University, Stockholm, Sweden.

Jian KangSchool of Architecture, University of Sheffield, Sheffield, United Kingdom.

SummaryScientific research on how people perceive, experience or understand the acoustic environment asa whole (i.e., soundscape) is still in development, both with regards to acoustic properties, as wellas personality and individual differences. In order to predict how people would perceive anacoustic environment, it is central to identify the underlying acoustic properties of soundscapes. Inthis study these properties were approached by investigating the visual similarity of colour printsof 50 audio spectrograms (time vs. frequency), representing audio recordings of a variety ofacoustic environments. In total, 15 female and 15 male students from the University of Sheffieldwere recruited to assess the 50 spectrograms by sorting them into groups based on how similarthey were perceived to be. A distance matrix, derived from the sorting data, was subjected to aMultidimensional Scaling analysis to map the underlying dimensions of similarity among thespectrograms, which are proposed to represent the underlying acoustic properties of thecorresponding acoustic environments. Three dimensions were identified. The first dimensionrelates to Distinguishable–Indistinguishable sound sources, the second dimension to Background–Foreground sounds, and the third dimension to Intrusive–Smooth sound sources. The results alsoshow that established acoustic parameters are inappropriate as indicators of acoustic environmentsand that further research is needed in this field.

PACS no. 43.50.+y, 43.90.+v

1. Introduction1

One of the first definitions of ‘soundscape’ isgiven in the Handbook for Acoustic Ecology (firstpublished in 1978) – “An environment of sound(or sonic environment) with emphasis on the wayit is perceived and understood by the individual, orby a society” [1]. The concept has attractedattention from various scientific disciplines:environmental acoustics, psychology, sociology,urban planning, and more. Due to its stronginterdisciplinary appeal it is a field of wideexperimentation. Important scientific contributionshave been published in the recent decade,

1(c) European Acoustics Association

proposing both theoretical models and practicalapproaches [2–4].

Still, scientific research on how peopleperceive, experience or understand the acousticenvironment as a whole (i.e., soundscape) is in itsinfancy, both with regards to environmentalaspects and to personality and individualdifferences. This is chiefly due to the difficultiesin translating the semantic definition intooperational definitions and measurements, as wellas lack of agreements among the involveddisciplines in this regard. In particular, there is alack of agreement on what are the central aspectsof soundscape, which is related to a lack ofagreement on the purpose of soundscape research.

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Axelsson et al. [5] proposed a comprehensivemodel for measuring the affective qualities ofsoundscape. The model is two-dimensional anddefined by four bipolar factors: the two orthogonalfactors Pleasantness and Eventfulness, which arelocated at a 45-degree rotation from the second setof orthogonal factors Calmness and Excitement.Cain [6] and colleagues [7] proposed a similarmodel where Vibrancy replaces Excitement, andwith Calmness and Vibrancy as the two mainfactors.

There have been several attempts to identifyrelationships between perceptual factors andestablished acoustic and psychoacousticparameters, such as A-weighted equivalent level,Loudness, Roughness, Sharpness and relatedpercentiles (e.g., [5,8,9]). Nevertheless, thisapproach is not necessarily successful, because theestablished acoustic and psychoacousticparameters are primarily developed for thepurpose of single sounds or sound sources, not forthe purpose of soundscape, nor for measuring theacoustic environment holistically.

The three main dimensions of environmentalsounds are frequency, sound-pressure level andtime. These dimensions can be represented invisual form as two-dimensional pictures known asspectrograms, for example, with time representedon the X-axis, frequency on the Y-axis and sound-pressure levels as a range of colours (see Figure1). To overcome the previous difficulties infinding what acoustic parameters could be usefulin soundscape research, the present studyinvestigated the perceived similarity of colourprints of spectrograms with the aim to identify theunderlying dimensions. The rationale is that thesedimensions should represent holistic properties ofthe acoustic environments included in the study.

2. Methods

2.1. ParticipantsThirty undergraduates and postgraduates at the

University of Sheffield, 18 to 33 years old,participated in the experiment (15 women and 15men, Mage = 24.2 years, SD = 4.8). The ethnicdistribution of the sample was 66.7% White orCaucasian and 33.3% Asian or Pacific Islander.Participants were selected from a sample of 100persons who completed an online surveycirculated via the established email list for studentvolunteers at University of Sheffield. Thequestions in the online survey were designed toachieve a diverse group of participants in terms of

gender, age and ethnic origin. All participants hadnormal colour vision as tested by the “Ishihara testfor colour deficiency” [10]. Because the goal wasto test only whether or not the participant had anormal colour appreciation, a reduced version ofthe test was used. It included 6 plates, selectedaccording to Ishihara’s instructions [10]. The 30participants who completed the experiment wererewarded for volunteering with a 10 GBP giftcard.

2.2. MaterialFifty recordings (30s) from Axelsson et al. [5]

were used for this experiment. They were selectedfrom a database of binaural recordings of outdooracoustic environments (London and Stockholm)with the aim to achieve a large variation of overallsound-pressure levels and urban/peri-urbanlocations. The fifty audio files (.wav) wereimported in Adobe Audition 3.0. For each binauralrecording, the spectrogram (time vs. frequency)was plotted for the right channel. Thespectrograms were set to have the time on the X-axis (0–30s, 1s steps) and the frequency on the Y-axis, with a linear scale (0–25kHz, 1kHz steps).Regarding the spectral controls for the colourscale of the sound-pressure level dimension, thesoftware default settings were used (132 dB range,512 frequency bands resolution, gamma index 2)and the three sampling colours were: yellow (RGB254, 250, 84 – width 67%), orange (RGB 249, 47,0 – width 76%) and purple (RGB 45, 7, 69 – width80%). The 50 spectrograms were printed in colouron glossy photo paper (18.5 × 4.5 cm, 150 dpiresolution). Figure 1 present three examples(Panels A–C) of the 50 spectrograms used in theexperiment.

Figure 1. Three examples (Panels A–C) ofspectrograms used in the experiment.

FORUM ACUSTICUM 2014 Aletta, Axelsson, Kang: Acoustic properties of Soundscape for urban design7–12 September, Krakow

2.3. Design and procedureThe experiment took place in an office room at

the School of Architecture, University ofSheffield. The design of the experiment consistedof a 2-stage data collection procedure: sorting andinterview. The participants took part individually.

First, the colour deficiency test was performedfor each participant. Successful participants wereadmitted to the following stage.

Seated at an office desk, every participant wasprovided with the 50 colour prints of thespectrograms as a stack of photographs mixed in aunique irregular order. They were instructed tosort the prints into mutually exclusive groupsaccording to the similarity of the spectrograms,and in as many groups as they wanted (2 being theminimum and 25 the maximum). In addition, theywere asked to pay attention to whether or not theydeveloped any specific sorting strategy. Thisinformation was required in the subsequentinterview. Participants were allowed to revise theirsorting throughout the experimental session,including the interview.

After completing the sorting task, theparticipants were interviewed. The purpose of theinterviews was to learn whether or not theparticipants had developed any soring criteria, andthen which they were. This information was usedto interpret the sorting results. During theinterview the experimenter took notes (cf. [11]).

The 30 experimental sessions lasted between 8and 45 minutes each (Mtime = 19.5 mins, SD = 8.9).There were no time restrictions.

2.4. AnalysisThe rationale for the method is that

spectrograms represent all acoustic information ofan acoustic environment, except the phase angle ofthe frequencies. By visual inspection of thespectrograms it is possible to decide to what degreethey resemble each other. Spectrograms that looksimilar should represent acoustic environments thatare similar. Consequently, the dimensions thatunderlie the similarity perceived among thespectrograms should represent holistic acousticproperties. These dimensions can be identified bythe aid of Multidimensional Scaling (MDS).

3. Results

The participants created between 3 and 17groups of spectrograms (Mgroups = 8.0, SD = 3.7).The sorting data was used to create a distancematrix based on how often all possible pairs of the50 spectrograms appeared in the same group,summed over all 30 participants (cf. [11]). Thedistance matrix was subjected to MDS (SPSS 21for Windows). By using the ALSCAL technique[12], six solutions, with one to six dimensions(stress values: 0.488, 0.257, 0.156, 0.109, 0.088,0.071), were extracted [13]. Based on a ‘screecriterion’ (cf. [14]), the three-dimensional solutionwas selected for further analysis.

Figure 2 presents the three-dimensional MDSsolution. Data points represent the 50spectrograms. In order to aid the interpretation ofthe three dimensions, clusters of spectrograms

Figure 2. Three-dimensional MDS solution

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were created through visual inspection of thespectrograms and by listening to thecorresponding audio recordings.

The first cluster contained spectrograms withpositive values in the first dimension (D1). In theinterviews they were often described as“dominated by horizontal stripes”, “representingall range of colours” or “with colours blurringinto each other”. Auditory inspection revealedsounds similar to white noise (typical soundsources were: fountain, heavy traffic noise, aircraftnoise), generally providing an acousticenvironment where different auditory featureswere indistinguishable.

The second cluster contained spectrogramswith negative values in D1. In the interviews theywere often described as having “spikes”, “mostlyvertical shapes” and “noticeable patterns”.Auditory inspection revealed clearly identifiablesound sources (e.g. birdsongs, footsteps,children’s voices) against a generally quietbackground, making them very distinguishable.Consequently, D1 was interpreted to representDistinguishable–Indistinguishable sound sources.

The third cluster had positive values in thesecond dimension (D2) and containedspectrograms that were referred to as “yellow” or“deep red”. In contrast, the fourth clustercontained spectrograms with negative values inD2, referred to as “purplish” or “dark”. Thissuggested that D2 was related to sound-pressurelevel. Auditory inspection of the correspondingaudio files revealed that D2 was associated withdistance of the sound sources from the listener: thethird cluster represented foreground sounds (soundsources were close) and the fourth clusterrepresented background sounds (sound sourceswere far). As a result, D2 was interpreted torepresent Background–Foreground sounds.

For the third dimension (D3), two separateclusters were created. The first of these twoclusters contained spectrograms with negativevalues in D3. These spectrograms were describedas “eventful” with “things going on” and“aggressive”. The second of the two clusterscreated for D3 contained spectrograms withmainly positive values. They were considered as“even”, “smooth” and “generally flat”. In thefirst case, sounds were characterised by anintrusive source, temporarily dominating theacoustic environment. In the second case, soundswere smooth and organic, regardless of thetemporal or spectral features. D3 was thereforeinterpreted to represent Intrusive–Smooth soundsources.

In order to provide further material for theinterpretation of the three dimensions, the acousticsignals that correspond to the 50 spectrogramswere subjected to further acoustic analyses. Foreach acoustic signal a set of 100 acoustic andpsychoacoustic parameters were calculated.

This included unweighted, A-weighted and C-weighted equivalent levels (Leq, LAeq, LCeq),Loudness (N), Sharpness (S), Roughness (R),Fluctuation strength (Fls), Tonality (Ton),statistical levels for the above mentionedparameters (P1, P5, P10, P25, P50, P75, P90, P95, P99),a measurement of the spectral variability (LCeq–LAeq), and the measurements of the temporalvariability (P1–P99, P5–P95, P10–P90, P25–P75). Datascreening revealed curvilinear relationshipsbetween the three dimensions and some of theacoustic and psychoacoustic parameters. For thisreason the base-10 logarithms were calculated forall of the 100 parameters, except for 6 of them thatincluded negative values.

Three stepwise linear regression analyses wereconducted, using D1, D2 and D3 as dependantvariables and the complete set of 194 parametersas independent variables (SPSS 21 for Windows).The three models explained 79.2%, 80.8% and39.0% of the variance in the correspondingdependent variable (D1, D2 and D3), respectively.

The strongest predictors of D1 were LA50 (t =9.89, p < 0.001), Log(S1–S99) (t = –3.85, p <0.001), Log(Fls25–Fls75) (t = –2.45, p = 0.018),and Log(S10–S90) (t = 2.28, p = 0.028); (F4,45 =42.79, p < 0.001, R2 = 0.79). The strongestpredictors of D2 were Log(N1–N99) (t = 6.07; p <0.001), Log(Fls95) (t = 4.03, p < 0.001), Log(S1) (t= 3.67, p = 0.001), Fls10 (t = –3.33, p = 0.002),and Fls99 (t = –2.33, p = 0.024); (F5,44 = 37.07, p <0.001, R2 = 0.81). The strongest predictors of D3were LA10–LA90 (t = –4.57, p < 0.001), Log(LA25–LA75) (t = 3.00, p = 0.004), and Fls99, (t = –2.10, p= 0.041); (F3,46 = 9.81, p < 0.001, R2 = 0.39).Table I presents the standardised regressioncoefficients () for all three models.

LA50 explained 38.9% of the variance in D1.When controlling for this variable, logmeasurements of variability in Sharpness [Log(S1–S99)] explained an additional 34.9% of thevariance. The positive relationship between D1and LA50 shows that there was more acousticenergy associated with the sounds interpreted asindistinguishable, compared to the soundsinterpreted as distinguishable. This indicates that,in the former case, several sound sources werepresent, possibly masking each other. It seemsreasonable that several sound sources are louder

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than one. The negative relationship between D1and Log(S1–S99) shows that as the variability inSharpness increased sounds were all moredistinguishable.

D2 was strongly and positively associated withvariability in loudness levels Log(N1–N99), whichalone explained 66.6% of the variance in D2. Thispositive relationship indicates that there is a largervariability in Loudness in sounds interpreted torepresent the foreground than in soundsinterpreted to represent the background, whichmakes sense.

D3 was chiefly associated with variability in A-weighted sound-pressure levels: LA10–LA90 andLog(LA25–LA75), which explained 21.5% and11.7% of the variance in D3, respectively.However, the two parameters work in oppositedirections, where the former had a negativerelationship and the latter a positive relationshipwith D3. This information is not particularlyhelpful in moving forward with the interpretationof D3. Thus, the analyses provided two usefuldimensions (D1 and D2).

4. Discussion

The purpose of this study was to explore theacoustic properties of acoustic environments,considered holistically. Measures of perceivedsimilarity of 50 spectrograms were subjected toMDS analysis. Three dimensions were identified:(D1) Distinguishable–Indistinguishable soundssources, (D2) Foreground–Background sounds,and (D3) Intrusive–Smooth sound sources. Linearregression analyses with D1, D2 and D3 asdependent variables and 100 acoustic andpsychoacoustic parameters as predictors showedthat D1 was positively associated with LA50 andnegatively associated with Log(S1–S99). D2 waspositively associated with Log(N1–N99). D3 wasmainly associated with variability in A-weightedsound levels, but the percentage of explainedvariance was low. For this reason it is notworthwhile to give D3 any further attention.

The importance of foreground and backgroundssounds to soundscape research has been raisedbefore. Andringa, and van den Bosch [15] arguesthat this is a central dimension of soundscape andperceived safety.

It is interesting that none of the three dimensionswere well-predicted by any single acoustic orpsychoacoustic parameter. In all cases acombination of at least two parameters was neededto reach a sizable percentage of explained variancein the dependent variable. In support of the

statements in the introduction, this shows that theestablished acoustic and psychoacousticparameters are inappropriate as indicators of anacoustic environment. More research is required inthis field, which must include mathematicalmodelling of acoustic properties of acousticenvironments. Ideally, there should be one acousticindicator per dimensions identified in this study.

With regards to the quality of the present studyit is reasonable to ask how many stimuli arenecessary to properly map all relevant acousticdimensions of acoustic environments. The theorybehind MDS states that at least nine stimuli areneeded to reach a definite MDS solution [13]. SPSScan handle 100 stimuli at most. The stimuli mustalso be selected to vary with regards to all relevantaspects. For this reason a wide selection isdesirable. As specified in the method section, the50 stimuli used in the present study represent awide selection of various acoustic environments inand around two large cities, which meets therequirements (see also [5]).

5. Conclusions

The main conclusions from this study are:

(1) The established acoustic and psychoacousticparameters are inappropriate as indicators ofan acoustic environment.

(2) Three dimensions were found, whichrepresent the underlying acoustic propertiesof acoustic environments: (D1)Distinguishable–Indistinguishable soundssources, (D2) Foreground–Background

Table I. Linear Regression Models for D1, D2 and D3

Model Predictor Coefficient ()

D1 LA50 .702

Log(S1–S99) –.937

Log(Fls25–Fls75) –.236

Log(S10–S90) .531

D2 Log(N1–N99) .544

Log(Fls95) .384

Log(S1) .361

Fls10 –.328

Fls99 –.174

D3 LA10–LA90 –1.241

Log(LA25–LA75) .810

Fls99 –.246

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sounds, and (D3) Intrusive–Smooth soundsources.

(3) Distinguishable sound sources have a lowermedian sound level (LA50) and a highervariability in Sharpness [Log(S1–S99)] thanindistinguishable sound sources.

(4) Background sounds have a low variabilityand foreground sounds a high variability inLoudness [Log(N1–N99)].

AcknowledgementThis research received funding through the PeopleProgramme (Marie Curie Actions) of the EuropeanUnion’s 7th Framework Programme FP7/2007-2013 under REA grant agreement n° 290110,SONORUS “Urban Sound Planner”, and by TheRoyal Society and The British Academy through aNewton International Fellowship to the secondauthor.

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