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 was weakly 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.

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, perceptual studies of water-generated sounds suggest great variation in listener preferences. For example, sounds from natural streams and sea waves tend to be perceived as pleasant, 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 perceived 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 contains 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).

Fountains have rising jets, downward falls, or a combination 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 indicators 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 envelope 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 content 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 soundscape quality (Axelsson et al., 2010), which is similar to previously proposed circumplex models of emotions (Russel, 1980), environments (Russel and Mehrabian, 1978), and sound quality (Västfjäll et al., 2002), all of which propose a fundamental “like–dislike” or “valence” dimension (cf. Kuppens et al., 2013). In the present study, unpleasant–pleasant scores of sounds were related to acoustic measures 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 properties for perceived pleasantness of water-fountain sounds.

Twenty-eight recording sites were selected from an initial 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 fountains. From these, 32 fountains, from 28 different sites, were selected for the experiment. The recordings were selected to 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 (Brüel & Kjær 4231 with conditioning amplifier 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 fountain where visitors could be expected to stay or sit. The distance 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 number 1. (The fountains are rank-ordered from the one generating the most pleasant sound, #1, to the one generating the least pleasant sound, #32, based on the results presented below, see Sec. III A.)

FIG. 1.

(Color online) Photos of a subset 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).

FIG. 1.

(Color online) Photos of a subset 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).

Close modal

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 overall 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 centroid (SC) of the 1/96-octave-band spectrum, that is, the frequency band for which the sum of lower band levels equals the sum of higher band levels. In addition, Aures' sharpness (Aures, 1985) was calculated (unit: acum), as this psychoacoustic measure has been used in several previous studies reporting both positive and negative relationships with preference 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 filter, but these analyses yielded results very similar to those presented below.

Recordings, photos, and perceptual data are available upon request to the third author.

1. Perceived similarity

A free sorting method was used to measure the perceived 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 dragging the icon to any place on the screen. Groups were created 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–eventful 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, they placed icons in an area of the screen defined by the orthogonal 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 listeners assessed each fountain sound on four bipolar scales, including an unpleasant–pleasant scale with nine categories, 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 unpleasant–pleasant scores. To simplify the presentation, unpleasant–pleasant scores will hereafter be called “pleasantness scores.” Note that negative “pleasantness scores” refer to “unpleasant” ratings on the bipolar scale.

All listeners conducted the experiment in the same order, starting with similarity sorting, followed by interactive pleasantness assessments, and, finally, pleasantness assessments on a bipolar scale. The three tasks were separated by several-minute pauses. Before the start of the experiment, audiograms were determined. The whole experiment took about 90 min to complete.

The listeners were tested individually in a soundproof and highly absorbent listening room (ambient sound level, <20 dB(A); reverberation time, T60, between 0.25 and 8 kHz, <0.1 s). The experimental sounds were presented using ambisonic technology (Gerzon, 1973; Poletti, 2005; Spors et al., 2013) with six loudspeakers placed in a hexagonal configuration surrounding the listener, with all loudspeakers located 1.6 m apart and 1.6 m from the listener at the center point of the hexagon (Fig. 2, left). The loudspeakers were mounted 1.1 m high to match the approximate position of the listener's ears when seated (Fig. 2, right). The sounds were presented at the same levels as recorded, that is, from 52 to 77 dB LAeq,30s. In the additional experiment (see Sec. III C), all sounds were set equal to 59 dB LAeq,30s. The presented levels were calibrated using a sound level meter (Svan 959, SVANTEK, Warsaw, Poland)

FIG. 2.

(Color online) Schematic setup of the six-channel ambisonic loudspeaker setup (left). Photo of listener (right).

FIG. 2.

(Color online) Schematic setup of the six-channel ambisonic loudspeaker setup (left). Photo of listener (right).

Close modal

The experiment was controlled by an application programmed in Pd-Extended 0.42.5 (Puckette, 1996), which instructed Reaper audio digital workstation software to play the sounds using an internal sound card (RME HDSPe MADI FX, Haimhausen, Germany) connected to external MADI to AES/EBU converters (RME ADI-6432R) that fed the active loudspeakers (Genelec 8130A, Iisalmi, Finland). The experimental sounds were evaluated on an Android tablet (Samsung Galaxy Note 10.1, Seoul, South Korea) using a software application developed for this study in java, Android 4.0.4 (Google, Mountain View, CA). The Pd application communicated with the other software components over a local network using the OSC protocol.

The main experiment involved 57 listeners (36 females and 21 males) aged 19–54 yr (mean age = 27 yr). In an additional experiment, described in Sec. III B, 36 listeners were tested (17 females and 19 males) aged 19–48 yr (mean age = 25 yr). All participants' hearing status was tested using an audiometer (Interacoustics model AD226 diagnostic audiometer, Assens Denmark); the listeners had hearing thresholds below 30 dB hearing level (HL) in their best ear for the tested frequencies 0.5, 1, 2, 3, 4, and 6 kHz.

In the sorting task, the listeners were free to create as many groups as they found appropriate, and between two and 16 groups were created (median = six groups). The number of times that two sounds were sorted into the same group was used as an ordinal measure of their perceived similarity. The similarity matrix of all sound pairs was subjected to ordinal MDS using the PROXSCAL algorithm (IBM Statistics SPSS 21), and a two-dimensional solution (Fig. 3) was found to fit the data well (S-stress values for one-, two-, and three-dimensional solutions were 0.24, 0.01, and 0.005, respectively).

FIG. 3.

Two-dimensional multidimensional scaling (MDS) solution. Symbols refer to three groups of sounds identified from the solution: 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).

FIG. 3.

Two-dimensional multidimensional scaling (MDS) solution. Symbols refer to three groups of sounds identified from the solution: 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).

Close modal

Distinct groups of sounds emerged from the MDS solution (Fig. 3). One group of sounds (circled solid) comprised recordings of fountains with large rising jets or large volumes of downward falling water. The qualitative descriptions given by the listeners and the authors' listening through the sounds suggested that the common perceptual characteristic of this group of sounds was a loud steady-state and waterfall-like sound character. At the other end of the 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 variable 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 fountains that generated moderately loud sounds, some of a steady-state character and others with a more variable temporal pattern. The sounds in this intermediate group were difficult to characterize in terms of common perceptual features, and the listeners used a variety of qualitative descriptors 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 characteristics. 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.

Figure 4 shows time-histories (left diagram) and narrow-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 compared to the soft-variable sounds, however there were also notable exceptions to this pattern, as discussed below in relation to spectral centroids.

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

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

Close modal

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 diagram), that is, high levels were associated with low pleasantness 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 relations between overall level, especially in terms of rank-order correlations 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 pleasantness scores (Pearson's linear coefficient of correlation, rP = −0.71, and Spearman's rank-order coefficient of correlation, rS = −0.72). 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 envelope but also to sounds' overall level (and thereby 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.

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 rP) and rank-order (Spearman's rS) 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. 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 rP) and rank-order (Spearman's rS) 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).

Close modal

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 measures 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 were set 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 discernible in the solution obtained in the equal-SPL experiment. 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 notwithstanding, the results suggest that equating the sounds SPLs did not drastically influence how the sounds were sorted in terms of perceived similarity.

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), 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. 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), 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.

Close modal

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-SPL experiment 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.

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 rP) and rank-order (Spearman's rS) 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 most pleasant (1) to least pleasant (32), based on the result of the first experiment.

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 rP) and rank-order (Spearman's rS) 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 most pleasant (1) to least pleasant (32), based on the result of the first experiment.

Close modal

Note, however, that there was a moderately strong relationship between the two experiments pleasantness scores (leftmost diagram of Fig. 7), suggesting that the equal-SPL procedure did preserve sound characteristics relevant for perceived pleasantness. In particular, several of the sounds from fountains generating loud steady-state sounds were still 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 pleasantness scores was low and not statistically significant. Thus, eliminating the variability in overall level strongly reduced the correlation between temporal variability and pleasantness scores observed in the first experiment (cf. Fig. 5, middle 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 rP = −0.38 and rS = −0.49, respectively (excluding sound #20 as a potential outlier would increase these coefficients to rP = −0.53 and rS = −0.60). These correlations were higher than observed in the first experiment (cf. rightmost diagram Fig. 5), suggesting 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 pleasantness 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 sharpness and SC (rP = 0.86). The negative coefficients show that for this set of sounds, amount of high frequency sounds, as reflected in high values on SC and sharpness, was associated with lower pleasantness scores.

FIG. 8.

Average pleasantness ratings of water-fountain sounds in the additional equal-SPL experiment as a function of the psychoacoustic measure sharpness. Statistics and p-values refer to linear (Pearson's rP) and rank-order (Spearman's rS) 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.

FIG. 8.

Average pleasantness ratings of water-fountain sounds in the additional equal-SPL experiment as a function of the psychoacoustic measure sharpness. Statistics and p-values refer to linear (Pearson's rP) and rank-order (Spearman's rS) 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.

Close modal

In this study, a set of 32 water-fountain sounds recorded in urban public spaces were assessed with regard to perceived 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 was strongly related to their overall SPLs and temporal variability, 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 pleasantness assessments. The additional experiment also demonstrated that the unpleasant sounds from some of the large fountains remained unpleasant even after equalizing their SPLs. This suggests that these fountains generated sounds that have an inherently unpleasant sound character independent of their overall SPL.

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 histories. 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; Jeon et al., 2012; Galbrun and Ali, 2013), which have reported relationships between preference-related attributes and parameters related to the sound's spectral envelope (including the psychoacoustic indicator sharpness, discussed further below). This discrepancy may partially be related to the limited variation in spectral envelopes of the present experiment'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 (Galbrun and 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 probably 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) conducted 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 spectral 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 77 dB 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 dominate the perception to the extent that less salient variation in spectral envelope has little influence on pleasantness assessments. 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 experiment. 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 (rP = 0.86) of sounds in the present study's equal-SPL experiment. 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 various water-generated sounds, including waterfall, fountain, and stream sounds. In contrast, Watts et al. (2009) reported a positive 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 positive association 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-significant) 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 recordings 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 with the 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.

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 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 contribution 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.

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 information 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 loudspeaker presentation of soundscapes. A novelty of the present 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 difference between soft variable and loud steady-state sounds from water fountains.

The following conclusions can be drawn from the results of the present study:

  1. Distinct groups of water-fountain sounds were identified: 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 loudness. From a soundscape perceptive, it may be advisable to avoid fountain designs that generate such sounds.

This research was conducted in the Sound Cities research program, funded by the Marianne and Marcus Wallenberg foundation.

1.
Aures
,
W.
(
1985
). “
Berechnungsverfahren für den sensorischen wohlklang beliebiger schallsignale” (“Calculation methods for perceived quality of sound signals”)
,
Acustica
59
,
130
141
.
2.
Axelsson
,
Ö.
,
Nilsson
,
M. E.
, and
Berglund
,
B.
(
2010
). “
A principal components model of soundscape perception
,”
J. Acoust. Soc. Am.
128
,
2836
2846
.
3.
Axelsson
,
Ö.
,
Nilsson
,
M. E.
,
Hellström
,
B.
, and
Lundén
,
P.
(
2014
). “
A field experiment on the impact of sounds from a jet-and-basin fountain on soundscape quality in an urban park
,”
Landscape Urban Plann.
123
,
49
60
.
4.
Berglund
,
B.
,
Preis
,
A.
, and
Rankin
,
K.
(
1990
). “
Relationship between loudness and annoyance for ten community sounds
,”
Environ. Int.
16
,
523
531
.
5.
Coxon
,
A. P. M.
(
1999
).
Sorting Data: Collection and Analysis
(
Sage
,
London
), pp.
1
98
.
6.
De Coensel
,
B. D. C. B.
,
Vanwetswinkel
,
S.
, and
Botteldooren
,
D.
(
2011
). “
Effects of natural sounds on the perception of road traffic noise
,”
J. Acoust. Soc. Am.
129
,
EL148
EL153
.
7.
Dramstad
,
W. E.
,
Tveit
,
M. S.
,
Fjellstad
,
W.
, and
Fry
,
G. L.
(
2006
). “
Relationships between visual landscape preferences and map-based indicators of landscape structure
,”
Landscape Urban Plann.
78
,
465
474
.
8.
Galbrun
,
L.
, and
Ali
,
T. T.
(
2013
). “
Acoustical and perceptual assessment of water sounds and their use over road traffic noise
,”
J. Acoust. Soc. Am.
133
,
227
237
.
9.
Galbrun
,
L.
, and
Calarco
,
F. M.
(
2014
). “
Audio-visual interaction and perceptual assessment of water features used over road traffic noise
,”
J. Acoust. Soc. Am.
136
,
2609
2620
.
10.
Gerzon
,
M. A.
(
1973
). “
Periphony: With-height sound reproduction
,”
J. Audio Eng. Soc.
21
,
2
10
.
11.
Gygi
,
B.
,
Kidd
,
G. R.
, and
Watson
,
C. S.
(
2007
). “
Similarity and categorization of environmental sounds
,”
Percept. Psychophys.
69
,
839
855
.
12.
Hong
,
J. Y.
, and
Jeon
,
J. Y.
(
2013
). “
Designing sound and visual components for enhancement of urban soundscapes
,”
J. Acoust. Soc. Am.
134
,
2026
2036
.
13.
Jeon
,
J. Y.
,
Lee
,
P. J.
,
You
,
J.
, and
Kang
,
J.
(
2010
). “
Perceptual assessment of quality of urban soundscapes with combined noise sources and water sounds
,”
J. Acoust. Soc. Am.
127
,
1357
1366
.
14.
Jeon
,
J. Y.
,
Lee
,
P. J.
,
You
,
J.
, and
Kang
,
J.
(
2012
). “
Acoustical characteristics of water sounds for soundscape enhancement in urban open spaces
,”
J. Acoust. Soc. Am.
131
,
2101
2109
.
15.
Kuppens
,
P.
,
Tuerlinckx
,
F.
,
Russell
,
J. A.
, and
Barrett
,
L. F.
(
2013
). “
The relation between valence and arousal in subjective experience
,”
Psychol. Bull.
139
,
917
940
.
16.
Nasar
,
J. L.
, and
Li
,
M. H.
(
2004
). “
Landscape mirror: The attractiveness of reflecting water
,”
Landscape Urban Plann.
66
,
233
238
.
17.
Nilsson
,
M. E.
,
Alvarsson
,
J.
,
Rådsten-Ekman
,
M.
, and
Bolin
,
K.
(
2010
). “
Auditory masking of wanted and unwanted sounds in a city park
,”
Noise Control Eng. J.
58
,
524
531
.
18.
Nilsson
,
M. E.
,
Botteldooren
,
D.
,
Jeon
,
J. Y.
,
Rådsten-Ekman
,
M.
,
De Coensel
,
B.
,
Joo
,
Y.
,
Maillard
,
J.
, and
Vincent
,
B.
(
2014
). “
Perceptual effects of noise mitigation
,” in
Environmental Methods for Transport Noise Reduction
, edited by
M. E.
Nilsson
,
R.
Klæbo
, and
J.
Bengtsson
(
Taylor & Francis
,
New York
), pp.
195
219
.
19.
Poletti
,
M. A.
(
2005
). “
Three-dimensional surround sound systems based on spherical harmonics
,”
J. Audio Eng. Soc.
53
,
1004
1025
.
20.
Puckette
,
M.
(
1996
). “
Pure data: Another integrated computer music environment
,” in
Proceedings of the Second Intercollege Computer Music Concerts
, pp.
37
41
.
21.
Rådsten-Ekman
,
M.
,
Axelsson
,
Ö.
, and
Nilsson
,
M. E.
(
2013
). “
Effects of sounds from water on perception of acoustic environments dominated by road traffic noise
,”
Acta Acust. Acust.
99
,
218
225
.
22.
Russel
,
J. A.
(
1980
). “
A circumplex model of affect
,”
J. Pers. Soc. Psychol.
39
,
1161
1178
.
23.
Russel
,
J. A.
, and
Mehrabian
,
A.
(
1978
). “
Approach-avoidance and affiliation as functions of the emotion-eliciting quality of an environment
,”
Environ. Behav.
10
,
355
387
.
24.
Spors
,
S.
,
Wierstorf
,
H.
,
Raake
,
A.
,
Melchior
,
F.
,
Frank
,
M.
, and
Zotter
,
F.
(
2013
). “
Spatial sound with loudspeakers and its perception: A review of the current state
,”
Proc. IEEE
101
,
1920
1938
.
25.
Västfjäll
,
D.
,
Gulbol
,
M.-A.
,
Kleiner
,
M.
, and
Gärling
,
T.
(
2002
). “
Affective evaluations of and reactions to exterior and interior vehicle auditory quality
,”
J. Sound Vib.
255
,
510
518
.
26.
Watts
,
G. R.
,
Pheasant
,
R. J.
,
Horoshenkov
,
K. V.
, and
Ragonesi
,
L.
(
2009
). “
Measurement and subjective assessment of water generated sounds
,”
Acta Acust. Acust.
95
,
1032
1039
.
27.
White
,
M.
,
Smith
,
A.
,
Humphryes
,
K.
,
Pahl
,
S.
,
Snelling
,
D.
, and
Depledge
,
M.
(
2010
). “
Blue space: The importance of water for preference, affect, and restorativeness ratings of natural and built scenes
,”
J. Environ. Psychol.
30
,
482
493
.