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Similarity and pleasantness assessments of water-fountainsounds recorded in urban public spaces

Maria Rådsten EkmanG€osta Ekman Laboratory, Department of Psychology, Stockholm University, SE-10691 Stockholm, Sweden

Peter Lunden Department of Sustainable Built Environment, SP Technical Research Institute of Sweden, Box 857,

SE-50115 Borås, Sweden

Mats E. Nilssona)G€osta Ekman Laboratory, Department of Psychology, Stockholm University, SE-10691 Stockholm, Sweden

(Received 12 March 2015; revised 14 September 2015; accepted 18 October 2015; published online16 November 2015)

Water fountains are potential tools for soundscape improvement, but little is known about their

perceptual properties. To explore this, sounds were recorded from 32 fountains installed in urban

parks. The sounds were recorded with a sound-field microphone and were reproduced using an

ambisonic loudspeaker setup. Fifty-seven listeners assessed the sounds with regard to similarity and

pleasantness. Multidimensional scaling of similarity data revealed distinct groups of soft variable

and loud steady-state sounds. Acoustically, the soft variable sounds were characterized by low

overall levels and high temporal variability, whereas the opposite pattern characterized the loud

steady-state sounds. The perceived pleasantness of the sounds was negatively related to their

overall level and positively related to their temporal variability, whereas spectral centroid wasweakly correlated to pleasantness. However, the results of an additional experiment, using the same

sounds set equal in overall level, found a negative relationship between pleasantness and spectral

centroid, suggesting that spectral factors may influence pleasantness scores in experiments where

overall level does not dominate pleasantness assessments. The equal-level experiment also showed

that several loud steady-state sounds remained unpleasant, suggesting an inherently unpleasant

sound character. From a soundscape design perspective, it may be advisable to avoid fountains

generating such sounds. VC 2015 Author(s). All article content, except where otherwise noted, is

licensed under a Creative Commons Attribution 3.0 Unported License .

[http://dx.doi.org/10.1121/1.4934956]

[KVH] Pages: 3043–3052

I. INTRODUCTION

Water fountains are installed in many urban public

spaces. In addition to their visual qualities, fountains may

generate pleasant sounds, thereby improving the quality of

the acoustic environment or soundscape. However, percep-

tual studies of water-generated sounds suggest great varia-

tion in listener preferences. For example, sounds from

natural streams and sea waves tend to be perceived as pleas-

ant, whereas sounds from waterfalls tend to be perceived as

unpleasant (e.g., Rådsten-Ekman et al., 2013; Galbrun and

Calarco, 2014). This suggests that the auditory aspects of water fountains should be considered, as an unpleasant

sound may counteract the positive visual effects a fountain

have on the quality of its location. This study evaluated per-

ceived similarity and pleasantness of a large set of urban

water fountains. The purpose was to identify perceptual

dimensions of sounds generated by the fountains and to

assess correlations between perceived pleasantness of the

sounds and their basic acoustic properties.

Water features add to the visual attractiveness of areas

(Nasar and Li, 2004; Dramstad et al., 2006; White et al.,

2010). Listening experiments verify that adding water-fountain

sounds may increase the overall quality of the soundscape

(Jeon et al., 2010, 2012), but care must be taken as unpleasant

water sounds may detract from the overall soundscape quality

(Rådsten-Ekman et al., 2013). The ability of water-generated

sounds to partially or completely mask unwanted sounds, such

as road traffic noise, is limited to situations in which the two

sources have similar temporal and spectral characteristics

(Watts et al., 2009; Nilsson et al., 2010; De Coensel et al.,

2011; Galbrun and Ali, 2013). Road traffic noise typically con-tains sizeable low-frequency components; this makes many

water-generated sounds ill-suited as maskers, as their spectra

are dominated by higher-frequency energy. However, some

water features with high flow rates may generate broadband

sounds that can partially or completely mask road traffic noise

(Galbrun and Ali, 2013). A problem, however, is that such

water features may themselves generate unpleasant sounds; for

example, studies have suggested that waterfall-like structures

generate sounds considerably less pleasant than those of water

features with lower flow rates (Rådsten-Ekman et al., 2013;

Galbrun and Calarco, 2014).a)Electronic mail: [email protected]

J. Acoust. Soc. Am. 138 (5), November 2015 VC Author(s) 2015 30430001-4966/2015/138(5)/3043/10

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Fountains have rising jets, downward falls, or a combi-

nation of the two. Factors influencing their sound include

flow rate, falling water height, impact materials, and number

of jets (Watts et al., 2009; Galbrun and Ali, 2013). These

factors are related to various acoustic properties of the

sounds. Previous studies have explored various acoustic indi-

cators that may predict the perceptual properties of fountain

sounds. Overall level, the main determinant of perceived

loudness, is a main factor, as high loudness is associated

with low preference of water-generated sounds (Rådsten-

Ekman et al., 2013), and, generally, with high annoyance of

environmental noise (e.g., Berglund et al., 1990). Water-

generated sounds with high temporal variability have been

found to be more pleasant than sounds with a steady-state

character (Galbrun and Ali, 2013). The role of spectral enve-

lope is less clear. For example, both negative (Galbrun and

Ali, 2013) and positive relationships (Watts et al., 2009;

Jeon et al., 2012) have been reported between preferences of

water-generated sounds and the psychoacoustic measure

sharpness, which is related to amount of high frequency con-

tent of sounds.

The purpose of this study was to evaluate a large set of urban fountains recorded in public open spaces. Listeners

assessed the sounds in terms of their perceived similarity, to

obtain a representation of the sounds in perceptual space.

Similarity assessments are based on the perceived similarity

of sounds on salient or dominating perceptual dimensions

(e.g., Gygi et al., 2007). Thus, analyses of similarity data,

typically using multidimensional scaling (MDS), may give

insight into perceptually significant aspects of sounds, in the

present application a set of fountain-generated sounds. The

listeners also assessed the sounds in terms of their position

on the bipolar unpleasant–pleasant dimension. This is the

main dimension of Axelsson’s circumplex model of sound-

scape quality (Axelsson et al., 2010), which is similar to pre-

viously proposed circumplex models of emotions (Russel,

1980), environments (Russel and Mehrabian, 1978), and

sound quality (V€astfj€all et al., 2002), all of which propose a

fundamental “like–dislike” or “valence” dimension (cf.

Kuppens et al., 2013). In the present study, unpleas-

ant–pleasant scores of sounds were related to acoustic meas-

ures of overall level, variability over time and spectral

envelope. In an additional experiment, all sounds were

adjusted to an equal overall sound pressure level (SPL) to

specifically explore the role of spectral and temporal proper-

ties for perceived pleasantness of water-fountain sounds.

II. METHOD

A. Fountain sound recordings

Twenty-eight recording sites were selected from an ini-tial set of 61 sites in the Stockholm area. The main reasons

for excluding sites were that the fountains were turned off or

the presence of disturbing noise from construction work or

other noise sources. Eight of the chosen sites had more than

one fountain. In total, this resulted in recordings of 42 foun-

tains. From these, 32 fountains, from 28 different sites, were

selected for the experiment. The recordings were selected to

FIG. 1. (Color online) Photos of a sub-

set of the recorded fountains. Numbers

correspond to the fountain numbers in

the following result figures (rank-

ordered from most pleasant, 1, to least

pleasant, 32).

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obtain a large variety in fountain sounds and to exclude

recordings with prominent background sounds from people

talking, ventilation systems, road traffic, etc.

Each fountain sound was recorded with a four-channel

ambisonic microphone (Soundfield SPS200 microphone,

Marlow, UK) comprising four directional microphones in

a tetrahedral configuration and a single measurement

microphone (Br €uel & Kjær 4231 with conditioning ampli-

fier type 5935, Nærum Denmark). All microphone outputs

were fed into a Sound Device 788T digital audio recorder

(Reedsburg, WI).

Recordings were made at the location nearest the foun-

tain where visitors could be expected to stay or sit. The dis-

tance between microphone and fountain side was 0.5–1.5 m.

Figure 1 shows photos of several selected fountains.

Recording equipment is visible in the photo of fountain num-

ber 1. (The fountains are rank-ordered from the one generat-

ing the most pleasant sound, #1, to the one generating the

least pleasant sound, #32, based on the results presented

below, see Sec. III A.)

B. Acoustic analyses of experimental sounds

From each of the 32 fountain recordings, a 30-s excerpt

from the ambisonic recording was selected for the listening

experiment. The corresponding 30-s excerpts were extracted

from the one-channel measurement recordings and used for

the acoustic analyses. One of the fountain sounds (#20)

included a period of a slowly increasing level for about 7 s,

followed by a period of steady state level. Analyses of this

sound included only the steady-state period, as it could be

assumed that this dominated the listener responses to the

sound. Data analyses were also conducted with this sound

excluded and, unless otherwise stated, these analyses gave

results very similar to those presented below.

The experimental sounds were subjected to a variety of

analyses using the ArtemiS software, version 12 (HEAD

acoustics, Herzogenrath, Germany). In this article, the focus is

on results from analyses of A-weighted SPLs and narrow-band

spectra. Three basic measures were derived representing over-

all level, time variability, and spectral envelope. Overall level

was measured as the A-weighted equivalent continuous SPL

(LAeq,30s), variability was measured as the standard deviation

of instantaneous A-weighted SPLs (SDLA, time weighting

fast), and spectral envelope was measured as the spectral cent-

roid (SC) of the 1/96-octave-band spectrum, that is, the fre-

quency band for which the sum of lower band levels equalsthe sum of higher band levels. In addition, Aures’ sharpness

(Aures, 1985) was calculated (unit: acum), as this psycho-

acoustic measure has been used in several previous studies

reporting both positive and negative relationships with prefer-

ence of water-generated sounds (see Sec. I). Sharpness was

calculated from the sounds’ average spectra.

The analyzed sounds were high-pass filtered at 100 Hz

to reduce influence of ambient low-frequency components.

Analyses were also conducted using a 500 Hz high-pass fil-

ter, but these analyses yielded results very similar to those

presented below.

Recordings, photos, and perceptual data are available

upon request to the third author.

C. Perceptual measures

1. Perceived similarity

A free sorting method was used to measure the per-

ceived similarity of the fountain sounds. The instruction was

to sort sounds in groups based on perceived similarity using

as many groups as the listeners found appropriate (e.g.,

Coxon, 1999). The sounds were sorted using a software

application developed for this experiment. The listeners

could listen to a sound by clicking its icon, and then drag-

ging the icon to any place on the screen. Groups were cre-

ated by placing icons of similar sounds near each other on

the screen. The icons were assigned random numbers, which

differed between listeners. The listeners were free to listen to

each sound as many times as desired until a final sorting had

been achieved; they were then asked to verbally describe

what characterized the sounds in each sorted group of

sounds.

Each listener sorted the sounds once. The number of times two sounds were sorted into the same group was used

as a measure of their perceived similarity. Two sounds were

duplicated, and the number of times a sound and its copy

was sorted into the same group was used as a measure of the

reliability of the sorting procedure. One of the duplicate

sounds was sorted into the same group by 50 listeners and

the other duplicate by 54 listeners, suggesting that most of

the 57 listeners reliably followed the sorting instructions.

Analyses of the data excluding the few listeners who did not

sort duplicates in the same group yielded very similar results

to those presented below, which were based on data from all

listeners.

2. Perceived pleasantness

After the sorting task, the listeners assessed the sounds

with regard to attributes defined by two orthogonal bipolar

dimensions, unpleasant–pleasant versus uneventful–eventful,

that define Axelsson’s circumplex model of soundscape

quality (Axelsson et al., 2010). The focus of this article is on

the unpleasant–pleasant dimension, since it is more relevant

for assessments of single sources than the uneventful–event-

ful dimension which is more relevant for assessments of

multi-source soundscapes. Listeners assessed the sounds on

the unpleasant–pleasant dimension in two ways. First, using

a software application developed for this experiment, theyplaced icons in an area of the screen defined by the orthogo-

nal unpleasant–pleasant ( x-axis) and uneventful–eventful

( y-axis) scales. The icons were assigned random numbers

that differed between listeners. The listeners listened to a

sound by clicking its icon and were free to listen to each

sound as many times as desired. The final locations of the

sounds were saved as coordinates in the two-dimensional

space and the x-coordinate was used as the unpleasant–

pleasant scale value. Second, in a separate session, the listen-

ers assessed each fountain sound on four bipolar scales,

including an unpleasant–pleasant scale with nine categories,

J. Acoust. Soc. Am. 138 (5), November 2015 Rådsten Ekman et al. 3045


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scored from 4 through 0, to 4 (the other scales being

uneventful–eventful, chaotic–tranquil, and monotonous–

exciting). Assessments on the bipolar unpleasant–pleasant

scale agreed well with the unpleasant–pleasant values derived

from the interactive method. Therefore, averages of scores

from the two methods were used to calculate the final unpleas-

ant–pleasant scores. To simplify the presentation, unpleas-

ant–pleasant scores will hereafter be called “pleasantness

scores.” Note that negative “pleasantness scores” refer to

“unpleasant” ratings on the bipolar scale.

D. Procedure

All listeners conducted the experiment in the same

order, starting with similarity sorting, followed by interac-

tive pleasantness assessments, and, finally, pleasantness

assessments on a bipolar scale. The three tasks were sepa-

rated by several-minute pauses. Before the start of the

experiment, audiograms were determined. The whole experi-

ment took about 90 min to complete.

The listeners were tested individually in a soundproof

and highly absorbent listening room (ambient sound level,

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perceptual space was a set of sounds (circled plus) from

recordings of small fountains with one or a few small rising

jets. Their common perceptual characteristic was a soft vari-

able sound with a purling-rippling sound character. Between

these distinct groups of loud steady-state and soft variable

sounds was a set of sounds (open circles) from various foun-

tains that generated moderately loud sounds, some of a

steady-state character and others with a more variable tem-

poral pattern. The sounds in this intermediate group were

difficult to characterize in terms of common perceptual fea-

tures, and the listeners used a variety of qualitative descrip-

tors to describe these sounds (that often were sorted in

separate groups by individual listeners). Visual inspection of

the perceptual space (Fig. 3) suggests a cluster of sounds in

the lower-middle part of the space, but listening through

these sounds did not suggest any obvious common character-

istics. The most obvious feature of these sounds may simply

be their lack of perceptual characteristics typical of either

the loud steady-state or soft variable sound groups.

After the sorting task, the listeners assessed the sounds

with respect to perceived pleasantness. The numbers in Fig. 3

refer to the rank order the sounds from most to least pleasant,based on the average pleasantness scores. High numbers (low

pleasantness) are found among the loud steady-state group

(circled solid), whereas low numbers (high pleasantness) are

found among the soft variable group (circled plus). However,

there are exceptions to this rule. For example, sounds 7 and

8, which were among the 10 most pleasant sounds, are

located closer to the loud steady-state than the soft variable

group, whereas the slightly less pleasant sounds 10 and 13

are located firmly in the soft variable group.

B. Acoustic and psychoacoustic analyses

Figure 4 shows time-histories (left diagram) andnarrow-band spectra (right diagram) for a sample of sounds

from each of the three groups identified in the MDS solution

(Fig. 3). The time-histories of the sampled sounds illustrate

well the difference between the three groups of sounds in

terms of overall level and temporal variability: The three

sounds representing the group of soft-variable sounds (#1–3)

are characterized by lower levels and larger fluctuations than

the sounds repressing the middle group (#16–18), which, in

turn, have lower levels and larger variations than the sounds

from the loud steady-state group (#30–32). The difference in

overall levels between sounds is also visible by the vertical

position of their spectra (right diagram). The spectral shapes,

however, were not distinctly different across the three groups

of fountain sounds. There was a tendency for relatively more

energy in the high frequency part of the spectrum for the

loud steady-state sounds and moderately loud sounds com-

pared to the soft-variable sounds, however there were also

notable exceptions to this pattern, as discussed below in rela-

tion to spectral centroids.

Figure 5 explores how overall level (LAeq,30s), temporal

variability (SDLA) and spectral centroid (SC) was related to

the pleasantness scores. A strong negative relationship was

seen between pleasantness and overall level (leftmost dia-

gram), that is, high levels were associated with low pleasant-

ness scores. The reversed trend was seen for the relationship

between variability and pleasantness (middle diagram), that

is, high variability was associated with high pleasantness.

This relationship was almost as strong as the relationsbetween overall level, especially in terms of rank-order cor-

relations which were less influenced by the two sounds with

highest variability values (>2 dB, #7 and #4). For spectral

centroid, the relationship was weak (rightmost diagram).

The spectra of the least pleasant sounds, all of type loud

steady-state, had centroids about 3 kHz, but this was also

true for several moderately pleasant sounds. The three most

pleasant sounds, all of type soft variable, had their SC at

slightly lower frequencies, about 2.5 kHz, but pleasant

sounds were also found among those with high SC, notably

sound #4 with a SC of 3.7 kHz. The psychoacoustic indicator

sharpness was highly negatively correlated with pleasant-

ness scores (Pearson’s linear coefficient of correlation,

r P¼0.71, and Spearman’s rank-order coefficient of corre-

lation, r S¼0.72). This is not surprising, given the high

negative correlation between pleasantness and overall level,

and the fact that sharpness is not only related to spectral en-

velope but also to sounds’ overall level (and thereby

FIG. 4. (Color online) Time-histories: A-weighted SPL (fast) versus time (left panel) and 1/96-octave-band spectra (right panel) for sounds #1–3 (soft vari-

able), #16–18 (moderately loud), and #30–32 (loud steady-state).



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loudness). Correlations between perceptual attributes and

sharpness are therefore difficult to interpret in experiments

with large SPL variations; the sharpness measure is more

relevant for experiments with equal SPLs as discussed next.

C. Additional equal-SPL experiment

The sound’s overall level was strongly related to their

location in the perceptual space and to their pleasantness

scores. However, correlation does not imply causation, and

this is not least true for correlations between acoustic meas-

ures and environmental sounds. Listeners may have used

other perceptual characteristics that co-varied with the

sound’s overall SPL (LAeq,30s), such as temporal variability

or spectral envelope.

To control for the effect of overall level on perceived

similarity and pleasantness, an additional experiment was

conducted in which the fountain sounds’ overall SPLs wereset equal to 59 dB LAeq,30s. This procedure left unchanged

the sounds’ variability (SDLA) and spectral centroid (SC).

The SPL-equalized sounds were assessed using the

same methodology as in the main experiment, but with a

new group of listeners. Data were analyzed in the same way

as in the first experiment described above. A two-

dimensional MDS solution was again found to fit the

similarity-sorting data well (S-stress values for one-, two-,

and three-dimensional solutions were 0.04, 0.01, and 0.007,

respectively). The solution is shown in Fig. 6. The SPL

equalization did not drastically change the relative location

of the experimental sounds in the perceptual spaces. That is,

the three clusters of sounds identified in the first experiment(circled plus, open circle, and circled solid) were still dis-

cernible in the solution obtained in the equal-SPL experi-

ment. Exceptions include sounds 7 and 24, which moved

from the moderately loud group to the group of fountains

generating loud steady-state sounds, and sound 27, which

moved in the opposite direction. These exceptions notwith-

standing, the results suggest that equating the sounds SPLs

did not drastically influence how the sounds were sorted in

terms of perceived similarity.

In contrast, the SPL equalization considerably changed

the relative pleasantness ratings of the sounds. In particular,

several sounds from the fountains producing soft variable

sounds were now assessed as less pleasant than sounds from

the group of fountains generating moderately loud sounds.

This is seen in the leftmost diagram of Fig. 7, which plots

the pleasantness ratings from the additional equal-SPLexperiment as a function of the corresponding ratings from

the first experiment. The strongly reduced variability in

pleasantness scores in the additional equal-SPL experiment

( y-axis) compared to the first experiment ( x-axis) verifies the

conclusion form the first experiment that overall level was

the main determinant of the variability in pleasantness

scores.

Note, however, that there was a moderately strong rela-

tionship between the two experiments pleasantness scores

(leftmost diagram of Fig. 7), suggesting that the equal-SPL

procedure did preserve sound characteristics relevant for per-

ceived pleasantness. In particular, several of the sounds from

fountains generating loud steady-state sounds were still

FIG. 5. Average pleasantness ratings of water-fountain sounds as a function of overall SPL (LAeq,30s, leftmost diagram), standard deviation of A-weighted

instantaneous SPLs, (SDLA, middle diagram), and spectral centroid (SC, rightmost diagram). Statistics and p-values refer to linear (Pearson’s r P) and rank-

order (Spearman’s r S) coefficients of correlation. Symbols identify three groups of sounds (cf. Fig. 3): loud steady-state sounds (circled solid), moderately loud

sounds (open circles) and soft variable sounds (circled plus). Numbers rank-order the sounds from the most pleasant (1) to least pleasant (32).

FIG. 6. Two-dimensional multidimensional scaling (MDS) solution from

the additional equal-SPL experiment. Symbols identify three groups of

sounds identified based on the multidimensional scaling solution from the

first experiment (cf. Fig. 3): loud steady-state sounds (circled solid), moder-

ately loud sounds (open circles), and soft variable sounds (circled plus).

Numbers rank-order the sounds from most pleasant (1) to least pleasant

(32), based on the result of the first experiment.



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assessed as among the least pleasant sounds. This suggests

that the character of these sounds was inherently unpleasant

independent of their overall level.The correlation between temporal variability and pleas-

antness scores was low and not statistically significant. Thus,

eliminating the variability in overall level strongly reduced

the correlation between temporal variability and pleasant-

ness scores observed in the first experiment (cf. Fig. 5, mid-

dle diagram). This indicates that the high positive correlation

observed in the first experiment to a significant extent was

confounded by the sounds’ overall level.

In contrast, the correlation between spectral centroid

and pleasantness scores was moderately high in the equal-

SPL experiment (rightmost diagram of Fig. 7), with linear

and rank-order correlations of r P¼0.38 and r S¼0.49,

respectively (excluding sound #20 as a potential outlier

would increase these coefficients to r P¼0.53 and

r S¼0.60). These correlations were higher than observed in

the first experiment (cf. rightmost diagram Fig. 5), suggest-

ing that spectral envelope may play a role for pleasantness

scores in experiments where overall level does not dominate

the assessments.

To compare with previous studies (e.g., Galbrun and

Ali, 2013), sharpness values were also calculated for the

SPL-equalized sounds. For such sounds, sharpness mainly

captures variation in amount of high frequency content of

sounds. Figure 8 shows the relationships between pleasant-

ness of SPL-equalized sounds and sharpness. The pattern of

data and size of correlations is similar to the corresponding

figure for spectral centroid (cf. Fig. 7, rightmost diagram), as

would be expected from the high correlation between sharp-

ness and SC (r P¼

0.86). The negative coefficients show thatfor this set of sounds, amount of high frequency sounds, as

reflected in high values on SC and sharpness, was associated

with lower pleasantness scores.

IV. GENERAL DISCUSSION

In this study, a set of 32 water-fountain sounds recorded

in urban public spaces were assessed with regard to per-

ceived similarity and pleasantness. The perceptual space

obtained from the similarity sortings suggested perceptually

distinct groups of sounds with different acoustic profiles.

The perceived pleasantness of the water-fountain sounds wasstrongly related to their overall SPLs and temporal variabili-

ty, but weakly related to spectral centroid. However, in the

additional experiment, in which sounds were set equal in

overall SPL, a negative relationship was found between

pleasantness and spectral centroid or sharpness, suggesting

that spectral envelope may influence pleasantness scores in

experiments where overall level does not dominate pleasant-

ness assessments. The additional experiment also demon-

strated that the unpleasant sounds from some of the large

fountains remained unpleasant even after equalizing their

SPLs. This suggests that these fountains generated sounds

FIG. 7. Average pleasantness ratings of water-fountain sounds in the additional equal-SPL experiment as a function of pleasantness ratings from the first

experiment (leftmost diagram), standard deviation of A-weighted instantaneous SPLs (SDLA, middle diagram), and spectral centroid (SC, rightmost diagram).

Statistics and p-values refer to linear (Pearson’s r P) and rank-order (Spearman’s r S) coefficients of correlation. Symbols identify three groups of sounds identi-

fied based on the multidimensional scaling solution from the first experiment (cf. Fig. 3): loud steady-state sounds (circled solid), moderately loud sounds

(open circles), and soft variable sounds (circled plus). Numbers rank-order the sounds from most pleasant (1) to least pleasant (32), based on the result of the

first experiment.

FIG. 8. Average pleasantness ratings of water-fountain sounds in the addi-

tional equal-SPL experiment as a function of the psychoacoustic measure

sharpness. Statistics and p-values refer to linear (Pearson’s r P) and rank-

order (Spearman’s r S) coefficients of correlation. Symbols identify three

groups of sounds identified based on the multidimensional scaling solution

from the first experiment (cf. Fig. 3): loud steady-state sounds (circled

solid), moderately loud sounds (open circles), and soft variable sounds

(circled plus). Numbers rank-order the sounds from the most pleasant (1) to

least pleasant (32), based on the result of the first experiment.



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that have an inherently unpleasant sound character independ-

ent of their overall SPL.

A. Relation to previous research

The present results agree with those of previous studies

finding that water features in which large amounts of water

impact on water tend to generate unpleasant sounds, for

example, sounds from natural waterfalls (Rådsten-Ekman

et al., 2013), waterfall-like fountains (Galbrun and Ali, 2013),and large jet-and-basin fountains (Axelsson et al., 2014).

Sounds from such structures are characterized by high SPLs,

broadband frequency content and fairly steady-state time his-

tories. In contrast, pleasant water sounds typically have low

SPLs and variable time histories (Watts et al., 2009).

Examples include sounds from natural streams (Galbrun and

Ali, 2013), sea waves (Rådsten-Ekman et al., 2013), and

fountains with few and small jests, as examined in the present

study.

In the first experiment of the present study, there was no

strong relationship between pleasantness scores and the

sound’s spectral centroid. This stand in contrast to previous

studies on water-generated sounds (Watts et al., 2009; Jeonet al., 2012; Galbrun and Ali, 2013), which have reported

relationships between preference-related attributes and

parameters related to the sound’s spectral envelope (includ-

ing the psychoacoustic indicator sharpness, discussed further

below). This discrepancy may partially be related to the

limited variation in spectral envelopes of the present experi-

ment’s sounds (cf. Fig. 4) compared to previous studies. For

example, two previous studies used recordings of small

water features with a variety of materials, including water,

concrete, stones, and gravel, on which the running water

impacted. This variation in impact material causes large

between-sound variation in spectral composition (Galbrunand Ali, 2013), which could explain the stronger relationship

between preference and spectral envelope than found in the

present study of fountains, in all of which water was the

impact material. However, a more important factor is prob-

ably the limited variation in SPLs in the cited studies

compared to the first experiment of the present study. For

example, Galbrun and Ali (2013) used water generated

sounds normalized to 55 dB LAeq, Watts et al. (2009) con-

ducted separate experiments, each with a constant SPL

(ranging from 43 to 60 dB LAeq across experiments), and

Jeon et al. (2012) mixed road-traffic noise with water-

generated sounds normalized to 52 dB LAeq in one condition

and 72 dB LAeq in another condition. It is possible that spec-tral aspects mainly influences preference ratings under such

equal-SPL conditions, as suggested by the much stronger

correlation between pleasantness and spectral envelope in

the equal-SPL experiment of this study (all sounds set to

59 dB LAeq) compared to the first experiment (SPLs from 52

to 77dB LAeq). A large variation in SPLs implies a large

variation in perceived loudness, the main determinant of

perceived noise annoyance (e.g., Berglund et al., 1990). The

present results are consistent with the notion that loudness

also is a main determinant of unpleasant–pleasantness of

water-generated sounds, and that a large loudness-variation

(as in the first experiment of the present study) may domi-

nate the perception to the extent that less salient variation in

spectral envelope has little influence on pleasantness assess-

ments. From a basic research perspective, it may thus be

advisable to restrict the variation in overall SPL in listening

experiments to explore spectral predictors of preferences for

water generated sounds, as in this study’s equal-SPL experi-

ment. From an applied perspective, it is of course more

relevant to present sounds at realistic SPLs, as in this study’s

first experiment.

Several of the studies cited above reported relationships

between preference-related attributes of water-generated

sounds and the psychoacoustic indicator sharpness. As already

mentioned, these studies used water-generated sounds of

approximately equal SPLs. For such sounds, sharpness is

mainly a measure of spectral envelope, as was illustrated by

the high correlation between sharpness and spectral centroid

(r P¼ 0.86) of sounds in the present study’s equal-SPL experi-

ment. In that experiment, sharpness (and spectral centroid)

was negatively associated with perceived pleasantness. This

agrees with listening studies of Galburn and colleges (Galbrun

and Ali, 2013; Galbrun and Calarco, 2014) who used variouswater-generated sounds, including waterfall, fountain, and

stream sounds. In contrast, Watts et al. (2009) reported a posi-

tive correlation between preference and sharpness. Galburn

and Ali speculate that this inconsistency may be because they

used both upward and downward flows, whereas Watts et al.

(2009) used downward flows only, including low-sharpness

sounds that might have evoked negative associations of water

running down drains. Galbrun and Ali (2013) suggests that

sharpness might not be a key factor driving preferences for all

types of water features, whereas temporal variation might be,

in line with their finding of a positive relationship between

temporal variability and preference. This is an interesting idea

that should be explored further. It agrees with the positiveassociation between pleasantness and temporal variability in

the first experiment of the present study. However, it remains

to be seen whether temporal variability was a causal factor or

just a covariate, because the correlation between pleasantness

and temporal variability was much reduced (and non-signifi-

cant) in the additional experiment using SPL-equalized sounds.

In fact, spectral envelope measures (SC and sharpness) were

stronger related to pleasantness scores than temporal variability

in the equal-SPL experiment. Jeon et al. (2012) used record-

ings of fountains in public open spaces mixed with road traffic

noise and found a negative correlation between sharpness and

calmness, in line with Galbrun and Ali (2013) and in line withthe present results from the equal-SPL experiment. However,

Jeon et al. (2012) also reported a positive correlation between

sharpness and preference ratings of the same sounds. More

research is clearly needed to clarify the role of spectral factors

for preference ratings of water-generated sounds.

B. Implications

The whole idea of fountains is, of course, to enhance the

quality of the spaces where they are erected. Several of the

large fountains included in the present study were visually

attractive and obviously designed to produce an interesting



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and aesthetically attractive interplay between fountain

structure and water movement (see photos in Fig. 1).

Unfortunately, the results of the present study suggest that

several of these fountains generated unpleasant sounds,

which probably detracts from the fountains’ overall contribu-

tion to the spaces where they are installed. These results are

based on correlations and do not allow causal inferences.

This said, decreased flow rate would seem to be a good start

when seeking to improve the sound quality of problematic

fountains, because this would reduce loudness and increase

sound variability. At the same time, the soft and pleasant

purling-rippling sound of the small fountains examined here

would hardly be appropriate for a large fountain installed in

a vibrant or noisy urban space, where its sound could be

masked. Future research is challenged to consider the design

of large fountains that generate pleasant sounds that at the

same time are congruent with their size, structure, and

dynamics as well as their locations.

C. Strengths and limitations

A strength of this study is that it used recordings from a

large set of real fountains in urban public spaces. Though by

no means a random selection of fountains in the Stockholm

region, the set included a large variety of fountain types and

represents the types of fountain sounds one may encounter in

Stockholm or a similar modern city. The recordings were

made near the fountains, so the results cannot be generalized

to fountain sounds heard at greater distances where they

would be heard mixed with other prominent sounds in the

area. The present experiments were restricted to sounds in

the absence of visual information. Audio–visual interactions

may influence the results of listening experiments. However,

the effects of visual information on auditory perception are

small in most cases (Nilsson et al., 2014), although visual in-formation strongly influences the overall assessment of a

place (Hong and Jeon, 2013; Galbrun and Calarco, 2014).

The experiments used advanced audio reproduction based on

ambisonic technology, which allows for very realistic loud-

speaker presentation of soundscapes. A novelty of the pres-

ent experiments compared with previous studies of water-

generated sound is that a sorting methodology was used that

does not require predefined definition of the perceptual

attributes to which the listener should attend. In the present

application, the results suggested distinct perceptual differ-

ence between soft variable and loud steady-state sounds

from water fountains.

D. Conclusions

The following conclusions can be drawn from the

results of the present study:

(1) Distinct groups of water-fountain sounds were identi-

fied: soft variable sounds from small fountains and loud

steady-state sounds from larger fountains. Acoustically,

the soft variable sounds were characterized by low

overall levels and high temporal variability, whereas

the opposite pattern characterized steady-state sounds.

A third group of moderately loud fountain sounds

included both sounds with high and low temporal

variability.

(2) Spectral envelope may play a role for the pleasantness of

fountain-generated sounds, but mainly for assessments

of sounds of similar overall levels, for which a negative

correlation between the sound’s pleasantness and their

spectral centroid or sharpness was suggested.

(3) High flow-rate fountains generating steady-state sounds

seem to have an inherently unpleasant sound character

irrespective of their overall SPL and, thereby, their loud-

ness. From a soundscape perceptive, it may be advisable

to avoid fountain designs that generate such sounds.

ACKNOWLEDGMENTS

This research was conducted in the Sound Cities

research program, funded by the Marianne and Marcus

Wallenberg foundation.

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