Science of the Total Environment 482-483 (2014) 432–439

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Science of the Total Environment

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Assessment of rural soundscapes with high-speed train noise

Pyoung Jik Lee 1, Joo Young Hong, Jin Yong Jeon ⁎Department of Architectural Engineering, Hanyang University, Seoul 133-791, Republic of Korea

H I G H L I G H T S

• Viewing a preferred visual image reduces the annoyance from high-speed train noise.• Natural feature percentage, number of land types, and LAeq decide image preference.• High-speed train noise annoyance is affected by natural feature percentage and LAeq.• Zwicker's loudness is correlated with the annoyance from high-speed train noise.

⁎ Corresponding author at: Tel.: +82 2 2220 1795;E-mail address: jyjeon@hanyang.ac.kr (J.Y. Jeon).

1 Present address: EMPA, Swiss Federal Laboratories forM8600, Dübendorf, Switzerland.

0048-9697/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.scitotenv.2013.07.026

a b s t r a c t

a r t i c l e i n f o

Article history:Received 29 March 2013Received in revised form 17 June 2013Accepted 5 July 2013Available online 14 August 2013

Editor: P. Kassomenos

Keywords:Rural soundscapeHigh-speed train noiseAudio-visual interaction

In the present study, rural soundscapes with high-speed train noise were assessed through laboratory exper-iments. A total of ten sites with varying landscape metrics were chosen for audio-visual recording. The acous-tical characteristics of the high-speed train noise were analyzed using various noise level indices. Landscapemetrics such as the percentage of natural features (NF) and Shannon's diversity index (SHDI) were adoptedto evaluate the landscape features of the ten sites. Laboratory experiments were then performed with 20well-trained listeners to investigate the perception of high-speed train noise in rural areas. The experimentsconsisted of three parts: 1) visual-only condition, 2) audio-only condition, and 3) combined audio-visualcondition. The results showed that subjects' preference for visual images was significantly related to NF,the number of land types, and the A-weighted equivalent sound pressure level (LAeq). In addition, the visualimages significantly influenced the noise annoyance, and LAeq and NF were the dominant factors affecting theannoyance from high-speed train noise in the combined audio-visual condition. In addition, Zwicker's loud-ness (N) was highly correlated with the annoyance from high-speed train noise in both the audio-only andaudio-visual conditions.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

The use of high-speed trains began in the 1960s, particularly inJapan and Europe, and many high-speed rail lines are currentlyunder construction worldwide. As high-speed trains have becomemore popular, the associated noise problems have grown to becomenational issues in many countries. In particular, residents of ruralareas often complain of the noise caused by high-speed trainsbecause trains travel at their maximum speeds in these areas.

Noise problems caused by high-speed trains in rural areas havebeen investigated mainly by deriving the dose–response curvesusing social surveys and measurements of sound pressure levels(Lambert et al., 1996; Yano et al., 2005; Chen et al., 2007). Laboratoryexperiments were also conducted to determine the annoyance of

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noise from high-speed trains, and the relationships between annoy-ance and acoustic indicators such as ASEL,2 LAeq, and percentilenoise level have been derived (Fastl and Gottschling, 1996; Vos,2004; De Coensel et al., 2007). However, the perceptions ofhigh-speed train noise in rural areas cannot be explained only bythe sound pressure levels because non-acoustical factors, includingthe rural landscape, may also influence human perception (Jeon etal., 2011; Brown et al., 2011). Therefore, non-acoustic factors as wellas acoustic features should be considered when investigating the per-ception of high-speed train noise in rural areas.

Most previous studies on rural areas have focused on their visualaspects without considering non-visual cues (Arriaza et al., 2004),and few previous studies have investigated the auditory perception ofrural areas. Qualitative linkages between the rural landscape structureand daily sound patterns have been investigated (Matsinos et al.,2008), and the power spectral densities of loudness and pitch have

2 Abbreviations: ASEL: A-weighted sound exposure level; LAeq: A-weighted equiva-lent sound pressure level.

433P.J. Lee et al. / Science of the Total Environment 482-483 (2014) 432–439

been analyzed to characterize the stimuli recorded in rural areas (DeCoensel et al., 2003). A rural soundscape has also been determinedusing semantic descriptors and physical measures, and LA50 as thebackground noise level has been suggested as an indicator for ruralareas (De Coensel and Botteldooren, 2006). In addition, subjectiveresponses to noise in quiet rural areas and the countryside have beenexplored through surveys and auditory experiments (Brambilla andMaffei, 2006; Lam et al., 2010). However, rural areas with high-speedtrain noise have not yet been investigated as a concept of soundscape.

This study aimed to investigate the perception of high-speed trainnoise in rural areas with regard to both acoustic and non-acousticfactors. Audio-visual recordings were taken in rural areas with high-speed trains, and both the acoustic and non-acoustic factors of thestimuli were analyzed. To characterize the non-acoustic aspects ofrural soundscapes, landscape metrics that were previously used inlandscape studies were adopted. Laboratory experiments were thenperformed to examine the subjective responses to high-speed trainnoise in rural areas. On the basis of the laboratory experiments, theeffects of the acoustic and non-acoustic factors on the perception ofhigh-speed train noise and the interaction between the audio andvisual images were investigated.

2. Materials and methods

2.1. Field measurement

2.1.1. Noise sourceField measurements were conducted to record noise in rural areas

in Korea. The dominant noise source in this study was a high-speedtrain called the Korea Train eXpress (KTX). The KTX was designedto carry passengers at a regular speed of 300 km/h; however, noiserecording was conducted when the trains ran at around 250 km/h.Currently, two types of KTX trains are running in Korea: KTX-1,built in 2004, and KTX II (KTX-Sancheon), constructed in 2010.Here, KTX II trains were studied to avoid the effects of differenttypes and ages of trains on the noise. Therefore, the color, shape,and dimensions of the trains were kept constant.

2.1.2. Site selectionRural areas adjacent to high-speed train railways were selected by

considering nine landscape features that have been used to character-ize landscapes in previous studies (Dramstad et al., 2006; Rogge et al.,2007; De Coensel and Botteldooren, 2006). The landscape featureswere classified as either natural or artificial. Natural features includeagricultural crops, mountains, forests, and bodies of water such aslakes and rivers, whereas artificial features include buildings, trafficroads, elevated railways, tunnels, and noise barriers. A total of tensites with different combinations of natural and artificial landscapefeatures were selected for field measurements. As listed in Table 1,agricultural crops were a dominant feature, but the other landscapefeatures included in the sites varied. For instance, the landscape of

Table 1Combinations of landscape features at each site.

Sites

1 2 3 4 5 6 7 8 9 10

Natural features 1 Agricultural crops ○ ○ ○ ○ ○ ○ ○ ○ ○ ○2 Mountain ○ ○ ○ ○ ○3 Forest ○ ○ ○ ○4 Bodies of water ○ ○ ○ ○

Artificial features 5 Building ○ ○ ○ ○6 Traffic road ○ ○ ○ ○7 Elevated railway ○ ○ ○ ○8 Tunnel ○ ○9 Noise barrier ○ ○ ○ ○ ○

site #2 consists of five artificial and three natural features, whereassite #10 has only three natural features. Visual images of the tensites are shown in Fig. 1.

2.1.3. Audio-visual recordingsAudio-visual recordings were conducted from 11:00 am to

3:00 pm, assuming that outdoor activities are most frequent duringthis period. The recordings were taken in May 2010 over four dayswith clear weather. Binaural recordings were made using a dummyhead (Type 4100, B&K) with a height of 1.6 m and DAT (PC208Ax,Sony). Each recording included the passing of one train from the leftside of the receiver. Visual images were also taken using a digitalcamera (COOLPIX P5000, Nikon) at a height of 1.6 m. At each site,around 20 visual images were taken from different angles, and 360°panoramic views were constructed. During the recordings, thedummy head and digital camera were positioned 100 m away fromthe railway, and the dummy head faced the source so that each earhad the same distance from the source.

2.1.4. Analysis of audio-visual recordingFor the analysis of the acoustical characteristics of the stimuli, 45-s

recordings including high-speed train noise were extracted from thefull audio recordings. The noise levels measured at each site are listedin Table 2. Although the distance between the receiver and the rail-way was the same (100 m) at each site, the A-weighted equivalentsound pressure levels (LAeq,45s) showed large variation from 69 to89 dBA. The reason may be that the real source-to-receiver distanceswere not kept constant as the height of the railway track changed ateach site, and road traffic noise, as well as structures near the railwaytrack, such as noise barriers and buildings, may influence the soundpressure levels. Site #6, with an elevated railway, generally had thehighest noise levels, whereas site #9, with a noise barrier, showedthe lowest levels. In addition, the L10–L90 values varied widelyowing to different background noise levels. In particular, the L10–L90value of site #2 was much smaller than that of the other sites becausea nearby highway contributed about 70 dBA to the background noiselevel. In addition, the LA50 levels for 10-s ambient noise varied from42.1 to 70.2 dBA, and were larger than 41 dBA, the limit for quietrural soundscapes (De Coensel and Botteldooren, 2006).

Fig. 2 illustrates the frequency characteristics of the recordedhigh-speed train noise. Although the noise levels varied across thesites, all of the stimuli showed similar tendencies. The spectral char-acteristics of high-speed train noise are found to be governed mainlyby high-frequency and low-frequency components (Mellet et al.,2006). Peaks over 2 kHz could be generated by aerodynamic noisedue to turbulence around the train and electric motors, whereaspeaks below 63 Hz are most likely engine noise.

The esthetic characteristics of visual images have been investigatedin soundscape studies (Carles et al., 1992; Viollon et al., 2002; Jeon etal., 2011), and visual images have been reported to significantly affectsoundscape perception (Viollon et al., 2002; Pheasant et al., 2010). Arecent study proposed using NF (the percentage of natural features)to explain the effects of visual images on the perception of tranquility(Pheasant et al., 2008). However, studies in soundscape researchhave rarely investigated physical indicators representing landscapecharacteristics. On the other hand, many landscape metrics obtainedfrommaps and photographs have been used in landscape studies to ex-amine which metric influences subjective responses to landscapes(Magurran and Magurran, 1988; Wiens, 1989; Fjellstad et al., 2001;Dramstad et al., 2006; Antrop and Van Eetvelde, 2000). In this study,six landscape metrics and NF were used to evaluate the visual images.The six landscape metrics were 1) Shannon's diversity index (SHDI),2) the heterogeneity index (Hix), 3) the number of patches, 4) thenumber of land types, 5) the open area, and 6) the percentage ofopenness.

Fig. 1. Sites selected for field measurements.

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NFwas calculated using a panoramic photo at each site. To estimatethe percentage of natural features, a 10 × 10 lattice was overlaid on 10pictures making up a panoramic photo, and the ratio of the total num-ber of grid squares and the number of grid squares occupied by naturalfeatures (vegetation and agricultural land, excluding the sky) wasobtained. Fig. 3 shows an example of a single photo with a lattice.Two metrics representing the diversity and heterogeneity of landscapetypes, the SHDI and Hix, were calculated using aerial photos providedfrom a DAUM local map (http://local.daum.net/map/index.jsp). TheSHDI can be calculated by

H ¼Xs

i¼1

− Pi � lnPið Þ; ð1Þ

where Pi represents the fraction of the entire population made up ofspecies i, and s indicates the number of species encountered(Magurran and Magurran, 1988). The Hix is calculated as

Hix ¼ 1−

Xn

i¼1i≠ j

Xn

j¼1

wijcij

Xn

i¼1i≠ j

Xn

j¼1

wij

; ð2Þ

where wij is the binary weight, which is set to 1 if i and j are neighborsand 0 otherwise; cij is the binary similarity index, which is set to 1 if iand j are identical and 0 otherwise (Fjellstad et al., 2001). A largerSHDI indicates that the area consists of more diverse land types, and alarger Hix indicates that the landscape ismore heterogeneous. Themin-imum heterogeneity value is zero when all points have the same landtype, and the maximum value is one when the points in every pair fallon different land types. The difference between the SHDI and the Hix

Table 2Noise levels of the 45-s recordings at each site.

Site 1 2 3 4 5 6 7 8 9 10

ASEL [dBA] 98.4 97.8 100.5 100.4 98.2 105.5 89.1 94.0 85.5 91.3Leq,45s [dBA] 81.9 81.3 84.0 83.9 81.7 89.0 72.6 77.5 69.0 74.8Lmax [dBA] 88.8 88.1 92.2 91.2 89.7 97.6 77.9 85.9 76.7 84.2L10–L90 [dBA] 28.8 13.4 36.4 30.0 29.2 34.2 19.5 24.6 21.8 27.6N [sone] 36.2 43.1 33.2 43.3 32.3 53.1 27.9 31.8 19.1 18.9Nmax [sone] 83.8 71.8 79.5 109.0 95.7 154.0 57.2 60.3 42.6 45.7

is that the former is based on the area occupied by a particular landtype, whereas the latter indicates the spatial pattern of the landscape.The number of patches and number of land types were calculatedfrom aerial views and a land cover map and validated through fieldmeasurements. In this study, the landscape was classified into eightland types according to land use. The number of patches, that is, patchesof land use types in a landscape, was then determined according to aland type analysis. To estimate the openness, all land types wereclassified as either clearly visible areas (open), such as rice fields, andobscured areas (closed), such as mountains and man-made structures.During the estimations, obstacles that blocked the sight were deter-mined from a panoramic viewshed at each site with the help of build-ings and telephone poles that enabled identification of the arearepresented in the views. The open area was then measured in squaremeters, and the percent openness was computed as the proportion ofthe open area to the total area of the viewshed on the map.

Table 3 shows the degree of variation in the visual images andviewsheds for different landscape metrics. Site #2, located between ahighway and an elevated railway, had the lowest NF (42.6%), whereasthe largest NF (98.1%) was observed at site #10, which consisted main-ly of forest and patches of agricultural land. The mean values of theSHDI and Hix for the ten sites were 1.483 (maximum of 2.01 at site#1 and minimum of 1.12 at site #10) and 0.25 (maximum of 0.44 atsite #3 and minimum of 0.11 at site #8), respectively. The number of

Fig. 2. Frequency characteristics of acoustic stimuli (solid black line: averaged soundpressure level for 10 stimuli).

Fig. 3. Example of calculation of the percentage of natural features (white box: naturalfeatures; black box: artificial features).

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patches, open area, and percentage of openness also varied consider-ably, whereas the number of land types was distributed over a smallrange of 4 to 7.

2.2. Laboratory experiments

2.2.1. Experimental designLaboratory experiments were performed to investigate the audio-

visual interaction in rural soundscapes with high-speed train noise.The experiments consisted of three parts: 1) a visual-only condition,2) an audio-only condition, and 3) a combined audio-visual condi-tion, where images were presented along with acoustic stimuli.Most previous studies evaluated the influence of audio-visual interac-tion on subjective responses when subjects were exposed to combi-nations of auditory and visual factors (Carles et al., 1992; Tamura,1997; Anderson et al., 1983). In this study, a visual image wasmatched to an auditory stimulus measured at each actual site, assum-ing that the ten sites varied greatly in their acoustic characteristicsand landscape features. Therefore, a total of ten panoramic picturesand acoustic stimuli obtained from the field measurements wereconstructed on the basis of the audio-visual recording data from thefield measurements.

During the experiments, subjective responses to visual images andacoustic stimuli were evaluated using an 11-point numerical scale(with 0 indicating “not at all” and 10 indicating “extremely”), as in pre-vious studies (Jeon et al., 2010; Jeon et al., 2011). In the visual-only

Table 3Minimum, mean, and maximum values for the different landscape metrics.

Minimum Mean Maximum

NF [%] 42.6 72.03 98.1Shannon's diversity index (SHDI) 1.12 1.483 2.01Heterogeneity index (Hix) 0.11 0.25 0.44Number of patches 7 14.4 20Number of land types 4 5.1 7Open area [m2] 16,968 109,469 458,711Percentage of openness [%] 43.1 71.8 96.2

condition, the esthetic qualities of each visual image were determined.The subjects were asked to evaluate visual images purely on the basisof esthetics according to the following instructions: “Please rate thestimuli (visual image) on an 11-point scale according to your preference”.For assessing annoyance in the audio-only and combined audio-visualconditions, the following instruction was used: “Please rate the acousticstimuli on an 11-point scale according to your annoyance.”

2.2.2. ProcedureDuring the experiments, subjects were seated in a testing booth.

Acoustic and visual stimuli were presented through headphones(Sennheiser HD 600) and a beam projector (Sony VPL-CX6), respec-tively. The sound reproduction system was calibrated using a pinknoise signal, which was measured at the location of the subject'sear. The sound stimuli were played directly from the PC via a D/A con-verter (MOTU 896 HD) to the headphones. The measurement resultsshowed that the frequency response of the sound reproductionsystem was flat within 3 dB in the range of 30–16,000 Hz. The projec-tor was located at the back of the room and above the participant inorder to reproduce a real-scale view. Visual stimuli were projectedonto a white screen (1.9 m wide and 1.4 m high) with a 1:1 angularscale. A panoramic view of each site was presented to the participantsas a 45-s video clip. The subjects participated in each test conditiontwice to enhance the reliability.

2.2.3. SubjectsSimilar to previous experimental works on auditory experiments

(Öhrström et al., 1980; Nilsson, 2007; Joynt and Kang, 2010), all thesubjects were university and graduate school students; however, inthis study, the subjects were selected after an interview to investigatetheir experience with traveling by high-speed train, experience withhearing high-speed train noise in outdoor environments, and noisesensitivity. After the interview, only subjects who had experiencedtravel by high-speed train and exposure to high-speed train noise inrural and suburban areas were chosen. In addition, all of them hadpreviously participated in laboratory-based auditory experimentsseveral times before this test. Therefore, they were consideredwell-trained listeners in auditory experiments. The subjects were 17males and 3 females. The number of subjects and the demographicswere similar to those used in previous studies (Joynt and Kang,2010; Vos, 2004; Jeon et al., 2010; Jeon et al., 2011) that dealt withperception of acoustic stimuli in a controlled laboratory setting. Thesubjects' ages ranged from 21 to 33 (mean: 25.8, standard deviation:1.1). Before the experiment, the hearing threshold level of eachparticipant was tested with an audiometer (Rion AA-77). The resultsshowed that all of the participants had normal hearing. The experi-ments were conducted in a testing booth (4 × 3 m2) where the back-ground noise level was approximately 25 dBA.

2.2.4. Statistical analysisAll statistical analyses were conducted using SPSS for Windows,

version 20.0 (SPSS, Inc., Chicago, IL, USA). Analysis of variance(ANOVA)was used to estimate the significance of differences in subjec-tive ratings across the type of experiment (audio only and combinedaudio-visual) and the sites. The relationships between subjective andobjective data were analyzed using Pearson's correlation coefficient(r). P values below 0.05 were considered statistically significant. Multi-ple regression analysis was also conducted to investigate the contribu-tions of the landscape and acoustical metrics to the subjectiveresponses.

3. Results

ANOVAwas used to estimate the significance of differences in sub-jective ratings across replications (first and second responses). Acomparison of the first and second ratings indicated that the mean

Table 4Correlation coefficients between preference scores and landscape metrics.

NF[%]

SHDI Hix Number ofpatches

Number ofland types

Open area[m2]

Percentage ofopenness [%]

0.65⁎ 0.01 0.09 −0.39 −0.71⁎ −0.18 −0.21

⁎ p b 0.05.

436 P.J. Lee et al. / Science of the Total Environment 482-483 (2014) 432–439

subjective scores obtained for the first responses were not significant-ly different from those obtained for the second responses (p N 0.10for all tests). Therefore, the subjective responses were averagedacross subjects and across replications in the detailed analysis.

3.1. Preference score

As shown in Fig. 4, the mean preference scores for the ten visualimages differed across the sites and varied from 4.0 and 7.4. The visu-al image of site #10 was the most highly preferred, whereas site #2was the least preferred.

To investigate which landscape metrics influenced the perceptionof the visual images, correlation analysis was performed. The correla-tion coefficients between the preference scores and the landscapemetrics are listed in Table 4. The preference scores were positivelycorrelated with NF, confirming the findings of a previous study(Pheasant et al., 2008), which reported that NF is highly related tosubjective responses to visual images. In addition, the preferencescores were negatively related to the number of land types. In otherwords, a higher percentage of natural features and a smaller numberof land types led to higher preference scores for visual images. How-ever, the relationship between the preference scores and the numberof land types requires further investigation using visual images withlarge variations in the number of land types. There were no signifi-cant correlations between the preference scores and other metricssuch as the SHDI, Hix, open area, and percentage of openness. Thisresult is inconsistent with that of a previous study (Dramstad et al.,2006), which reported that the number of patches and SHDI werehighly correlated with the preference scores for visual images. Thisis because that study dealt with only agricultural landscapes, andthe variations in these metrics were much larger than those in thisstudy. Another possibility is that only NF was calculated directlyfrom the panoramic view subtending the same visual angles fromthe fixed perspectives of the images shown to the subjects, whereasall the landscape metrics were determined mainly from the aerialphotos.

3.2. Annoyance scores

The mean annoyance scores obtained from the audio-only condi-tion and combined audio-visual conditions are plotted in Fig. 5. Thewhite columns and gray columns indicate the results from theaudio-only and audio-visual conditions, respectively. When audiostimuli were presented alone, sites #5, #7, and #9 were rated as lessannoying than the others. In contrast, the mean annoyance scores forsites #3, #4, and #6 had values higher than “5,”which can be regardedas a threshold of annoyance. A comparison of the results from theaudio-only and audio-visual conditions revealed that the difference in

Fig. 4. Preference scores for the visual-only condition.

the annoyance scores was statistically significant (p b 0.05). Overall,the annoyance scores for the combined audio-visual condition werelower than those for the audio-only condition. This indicates that thevisual images significantly affected the perception of high-speed trainnoise. However, the changes in the annoyance scores with the presen-tation of visual images depended on the characteristics of the visualimages. The annoyance scores for sites #6 and #8 were significantlylower when audio and visual stimuli were presented simultaneously,whereas the annoyance scores were slightly higher for sites #2, #4,#5, and #9.

ANOVA was used to estimate the significance of the differences inthe annoyance scores across the type of experiment (audio only andcombined audio-visual) and the sites (sites #1–#10). The main effectof the type of experiment on the annoyance scores was significant[F(1, 6) = 4.153, p b 0.05], and the main effect of the type of sitewas also significant [F(9, 259) = 18.585, p b 0.01]. The interactionsbetween the type of experiment and the type of site were also signif-icant (p b 0.05). A comparison of the annoyance scores for theaudio-only and combined audio-visual conditions revealed significantdifferences at sites #6, #8, #9, and #10 (p b 0.05).

To investigate the effect of visual images on the change in theannoyance scores, the correlations between the preference scores forthe visual images and the differences between the annoyance scoresfor the audio-only and audio-visual conditions were calculated. Therelationship was found to be statistically significant (Pearson's correla-tion coefficient r = 0.57, p b 0.05); this result is consistent with previ-ous studies (Viollon et al., 2002; Viollon and Lavandier, 2002; Pedersenand Larsman, 2008), in which a positively evaluated visual appearancewas found to cause a reduction in noise annoyance.

The correlation coefficients between the annoyance scores andacoustical metrics were calculated; the results are listed in Table 5.For the audio-only condition, the annoyance scores were highlycorrelated with Zwicker's loudness (N) as well as LAeq, Lmax, and L10.The correlation coefficients between them (except for Zwicker's loud-ness) were greater than 0.9. Similarly, the annoyance scores werehighly correlated with Zwicker's loudness (N), LAeq, Lmax, and L10 inthe combined audio-visual condition. However, the correlation coeffi-cients were slightly lower than those for the audio-only condition be-cause the subjects did not fully concentrate on the acoustic stimuliwhen presented with visual images.

Fig. 5. Annoyance scores for the audio-only and combined audio-visual conditions.

Table 5Correlation coefficients between annoyance scores and acoustical metrics.

Leq,45s[dBA]

Lmin

[dBA]Lmax

[dBA]L50[dBA]

L10[dBA]

N[sone]

Audio only 0.94⁎⁎ 0.16 0.93⁎⁎ 0.31 0.95⁎⁎ 0.84⁎⁎

Combinedaudio-visual

0.79⁎⁎ 0.32 0.72⁎ 0.48 0.78⁎⁎ 0.77⁎⁎

⁎ p b 0.05.⁎⁎ p b 0.01.

437P.J. Lee et al. / Science of the Total Environment 482-483 (2014) 432–439

To calculate the effects of the landscape and acoustical metrics onthe subjective responses, multiple regression analysis was conductedusing a linear combination of landscape and acoustical metrics. Thedependent variables were the preference scores (visual-only condi-tion) and annoyance scores (audio-only and combined audio-visualconditions) obtained from the laboratory experiments. The best combi-nations of variables with respect to the correlation between the subjec-tive responses and the landscape and acoustical metrics variedwith theexperimental conditions; the results are described by Eqs. (3)–(5).Even though the number of land types had larger correlation coeffi-cients than NF, it was not included in any regression equations becausethe range of the number of land types was too small compared withthat in previous studies (Magurran and Magurran, 1988; Wiens,1989; Fjellstad et al., 2001; Dramstad et al., 2006).

Preferencevisual only≈a1NF ð3Þ

Annoyanceaudio only≈a2LAeq ð4Þ

Annoyancecombined audio−visual≈a3LAeq þ b1NF ð5Þ

The standardized partial regression coefficients of variables a1 anda2 in Eqs. (3) and (4) were 0.76 and 0.14, respectively, and these co-efficients were statistically significant (p b 0.01). For Eq. (5), the stan-dardized partial regression coefficients of variables a3 and b1 were0.76 and −0.35, respectively, and these were also statistically signif-icant (p b 0.01 for a3 and p b 0.05 for b1). These results indicate that alarger LAeq and a smaller NF result in higher annoyance, and the effectof the sound pressure level on annoyance is larger than that of thenatural features of the visual images. Using these values, the obtainedtotal coefficients for Eqs. (3)–(5) were 0.71, 0.94, and 0.85, respec-tively, which again were all significant [p b 0.05 for Eqs. (3) and (5),and p b 0.01 for Eq. (4)].

Fig. 6. Running IACC for high-speed train noise (s

3.3. Relationship between landscape features and startling effects

Different combinations of landscape features are expected toaffect the acoustical characteristics of high-speed train noise. In par-ticular, the differences in site openness may influence the variationsin the rise time and the binaural characteristics of the noise. Becauserapid increases in the sound pressure level are known to cause highlevels of annoyance (Fastl, 2000; Fastl et al., 2009; Marshall andDavies, 2007), the startling reaction has often been taken into accountin the evaluation of high-speed train noise (De Coensel et al., 2007).In this study, various indicators related to the startling reactionwere adopted on the basis of previous studies (De Coensel et al.,2007; Fastl et al., 2009), including the increase in level (ΔL), pedestallevel (Lped), pedestal loudness (Nped), increase in loudness (ΔN), andrise times for noise level and loudness (tR and tR(N)). It was observedthat the rise time was negatively correlated with only the percentopenness (r = −0.69, p b 0.01) (i.e., the noise levels would increaseslowly in open areas).

3.4. Spatial characteristics of high-speed train noise

In addition, themagnitude of the interaural cross-correlation function(IACC) was analyzed to investigate the spatial characteristics of the bin-aural recordings, as in previous studies (Jeon et al., 2009; Shimokura andSoeta, 2011; Sakai et al., 2002). The measured values of the running IACCare shown in Fig. 6. The IACC increased quickly when high-speed trainswere approaching and departing because the noise came from limiteddirections, whereas the IACC values were lowwhen trains passed direct-ly in front of the receiver because the noise came from many directions(Fujii et al., 2001). The results of the correlation analysis indicated thatthe rise time of the IACC was correlated with the total area (r = 0.64,p b 0.01), but no parameters were significantly related to the annoyancescores. This can be explained by the fact that the annoyance scores de-pend mainly on the sound pressure levels and loudness.

3.5. Effects of psychoacoustic metrics on subjective ratings

The psychoacoustic metrics (loudness, roughness, sharpness, andfluctuation strength) of high-speed train noise were analyzed withPulse Software, ver. 12.6 (Brüel & Kjær) in the samemanner as in pre-vious studies (Lee et al., 2009; Jeon et al., 2012). As shown in Fig. 7a,the loudness values fluctuated with a large variation of 18.6 to53.1 sone. The variations in sharpness, roughness, and fluctuationstrength were smaller than that in loudness, but the maximum differ-ences in all the psychoacoustic metrics were greater than the just no-ticeable difference (JND, the difference threshold based on the

olid black line: averaged IACC for 10 stimuli).

Fig. 7. Mean values of psychoacoustic metrics: (A) loudness and sharpness and(B) roughness and fluctuation strength.

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criterion of 75% correct answers by participants) of each metric re-ported in a previous study (You and Jeon, 2008). This result corre-sponds to the findings on the frequency characteristics and soundpressure levels; namely, all the train noise showed similar tendencies,whereas the sound pressure levels varied across the sites. The resultsof correlation analysis between the annoyance scores and psycho-acoustic metrics showed that only loudness was highly correlatedwith the annoyance scores (r = 0.84 and r = 0.77 for theaudio-only and audio-visual conditions, respectively; p b 0.05 forboth). This indicates that loudness is the dominant factor influencingthe perception of high-speed train noise. The loudness was found tobe highly correlated with LAeq,45s (r = 0.88, p b 0.01). Similar to theresults for the acoustic metrics, the correlation coefficient betweenloudness and the annoyance scores decreased slightly when visualimages were included.

4. Discussion

The interaction between auditory and visual cues has been studiedto investigate the interaction between noise and vision using a globalstrategy (Carles et al., 1992; Tamura, 1997; Viollon et al., 2002), andto examine the specific effects of noise on vision (Anderson et al.,1983) and the effect of vision on noise (Kastka and Noack, 1987).However, most of these studies were conducted in a laboratorysetting with various combinations of noise and vision: Carles et al.(1992) adopted 32 combinations consisting of four sounds andeight landscapes, whereas Anderson et al. (1983) used 20 combina-tions of two outdoor images and ten meaningful sounds. In addition,recent studies (Tamura, 1997; Anderson et al., 1983; Viollon et al.,2002) have varied the visual images in terms of the specific context,such as the degree of tree coverage and degree of urban development.In contrast to previous studies, combinations of noise and visualimages were not applied in this study because it focused mainly onthe effect of visual images on the perception of noise at each site. Inaddition, the visual images used in this study were selected by consid-ering nine landscape features without any specific context. In contrast,

previous studies chose visual images in relation to specific contexts.Anderson et al. (1983) used a downtown street site and a completelywooded site, and Viollon et al. (2002) selected four images showingvarious degrees of urban development. Thus, the differences betweenthe visual images used in this study were less distinct than those be-tween the images used in previous studies. Therefore, the preferencescores for visual images presented in Fig. 4 do not show large varia-tions, and the effect of visual images on the change in the annoyancescores was much less than in previous studies (Anderson et al., 1983;Kastka and Noack, 1987; Viollon et al., 2002). Because this studyfound that NF is most suitable for explaining the effect of different com-binations of landscape features on the subjective response to the ruralsoundscape, it may be useful in future studies to vary the context ofvisual images in terms of the percentage of natural features.

To investigate the effects of non-acoustic factors on the subjectiveresponses, the frequency of visits to rural areas and the noise sensitivitywere evaluated using an 11-point numerical scale. The subjects werethen divided into two groups (low and high) according to their stan-dardized scores based on the probability density function of the scoresfor visitation frequency and noise sensitivity. The numbers of subjectsbelonging to the two groups were 11 and 9 for visitation frequencyand 12 and 8 for noise sensitivity. No significant difference was foundbetween groups in the subjective responses obtained under the threeconditions. The reason may be that representations of audio and visualstimuli in the laboratory setting cannot entirely reflect the real situa-tions that on-site observers would perceive as an individual trainpassed by. In addition, the laboratory experiments in this study didnot consider the cumulative effects of different numbers of trains andtimes of day that they pass by. Therefore, it would be necessary tovalidate the laboratory findings in the future through field experimentsin which the subjects could be exposed to noise for a long time.

5. Conclusions

Objective and subjective assessments of high-speed train noise inrural areas were conducted through field measurements and labora-tory experiments. It was found that, among landscape metrics, thepercentage of natural features (NF) and the number of land typeswere highly correlated with the preference scores of visual images.It was also shown that the annoyance scores depended mainly onLAeq when acoustic stimuli alone were presented to the subjects. Inthe combined audio-visual condition, the effect of the visual imageon the perception of noise was found to be significant, and the morepreferred visual image caused less annoyance from high-speed trainnoise. NF and LAeq were found to be major factors influencing annoy-ance, but the contribution of LAeq to the annoyance scores was largerthan that of NF.

In addition, the openness and total area significantly affected therise times of the high-speed train noise and IACC, respectively. More-over, it was observed that among the psychoacoustic metrics, onlyZwicker's loudness (N) was correlated with the annoyance scores.In the future, it will be necessary to introduce non-acoustic factorsthat are closely related to the perception of landscapes in ruralareas on the basis of a qualitative study and to conduct a field valida-tion test in real circumstances. In this study, small numbers ofwell-trained students with a limited age range participated; thus,additional experiments with more subjects and greater variation inage and profession would be beneficial to validate the findings ofthis study.

Acknowledgment

This research was supported by a grant (13PRTD-C061727-02)from the Construction & Transportation Technology AdvancementResearch Program funded by the Ministry of Land, Transport, andMaritime Affairs of the Korean government.

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References

Anderson L, Mulligan B, Goodman L, Regen H. Effects of sounds on preferences for out-door settings. Environ Behav 1983;15:539–66.

Antrop M, Van Eetvelde V. Holistic aspects of suburban landscapes: visual image inter-pretation and landscape metrics. Landsc Urban Plan 2000;50:43–58.

Arriaza M, Canas-Ortega J, Canas-Madueno J, Ruiz-Aviles P. Assessing the visual qualityof rural landscapes. Landsc Urban Plan 2004;69:115–25.

Brambilla G, Maffei L. Responses to noise in urban parks and in rural quiet areas. ActaAcust United Acust 2006;92:881–6.

Brown AL, Kang J, Gjestland T. Towards standardization in soundscape preferenceassessment. Appl Acoust 2011;72:387–92.

Carles J, Bernáldez F, de Lucio J. Audio-visual interactions and soundscape preferences.Landsc Res 1992;17:52–6.

Chen X, Tang F, Huang Z, Wang G. High-speed maglev noise impacts on residents: acase study in Shanghai. Transp Res D 2007;12:437–48.

De Coensel B, Botteldooren D. The quiet rural soundscape and how to characterize it.Acta Acust United Acust 2006;92:887–97.

De Coensel B, Botteldooren D, De Muer T. 1/f noise in rural and urban soundscapes.Acta Acust United Acust 2003;89:287–95.

De Coensel B, Botteldooren D, Berglund B, Nilsson M, De Muer T, Lercher P. Experimentalinvestigation of noise annoyance caused by high-speed trains. Acta Acust UnitedAcust 2007;93:589–601.

Dramstad WE, Tveit MS, Fjellstad W, Fry GLA. Relationships between visual landscapepreferences and map-based indicators of landscape structure. Landsc Urban Plan2006;78:465–74.

Fastl H. Railway bonus and aircraft malus: subjective and physical evaluation. St.Petersburg, Russia: the 5th int. symposium on transport noise and vibration; 2000.

Fastl H, Gottschling G. Subjective evaluation of noise emissions from Transrapid. Liver-pool, UK: Internoise; 1996.

Fastl H, Kerber S, Guzsvany N. Aspects of startling noises. Edinburgh, Scotland:Euronoise; 2009.

Fjellstad W, Dramstad WE, Strand GH, Fry GLA. Heterogeneity as a measure ofspatial pattern for monitoring agricultural landscapes. Nor Geogr Tidsskr2001;55:71–6.

Fujii K, Soeta Y, Ando Y. Acoustical properties of aircraft noise measured by temporaland spatial factors. J Sound Vib 2001;241:69–78.

Jeon JY, Lee PJ, Kim JH, Yoo SY. Subjective evaluation of heavy-weight floor impactsounds in relation to spatial characteristics. J Acoust Soc Am 2009;125:2987–94.

Jeon JY, Lee PJ, You J, Kang J. Perceptual assessment of quality of urban soundscapes withcombined noise sources and water sounds. J Acoust Soc Am 2010;127:1357–66.

Jeon JY, Lee PJ, Hong JY, Cabrera D. Non-auditory factors affecting urban soundscapeevaluation. J Acoust Soc Am 2011;130:3761–70.

Jeon JY, Lee PJ, You J, Kang J. Acoustical characteristics of water sounds for soundscapeenhancement in urban open spaces. J Acoust Soc Am 2012;131:2101–9.

Joynt JLR, Kang J. The influence of preconceptions on perceived sound reduction byenvironmental noise barriers. Sci Total Environ 2010;408:4368–75.

Kastka J, Noack R. On the interaction of sensory experience, causal attributive cogni-tions and visual context parameters in noise annoyance. Dev Toxicol Environ Sci1987;15:345–60.

Lam KC, Brown A, Marafa L, Chau K-C. Human preference for countryside soundscapes.Acta Acust United Acust 2010;96:463–71.

Lambert J, Champelovier P, Vernet I. Annoyance from high speed train noise: a socialsurvey. J Sound Vib 1996;193:21–8.

Lee PJ, Kim JH, Jeon JY. Psychoacoustical characteristics of impact ball sounds on con-crete floors. Acta Acust United Acust 2009;95:707–17.

Magurran AE, Magurran AE. Ecological diversity and its measurement, vol. 168.Princeton, NJ: Princeton University Press; 1988.

Marshall A, Davies P. A semantic differential study of low amplitude supersonicaircraft noise and other transient sounds. Madrid, Spain: 19th Intl. Congresson acoustics; 2007.

Matsinos YG, Mazaris AD, Papadimitriou KD, Mniestris A, Hatzigiannidis G, Maioglou D,et al. Spatio-temporal variability in human and natural sounds in a rural landscape.Landsc Ecol 2008;23:945–59.

Mellet C, Létourneaux F, Poisson F, Talotte C. High speed train noise emission: latestinvestigation of the aerodynamic/rolling noise contribution. J Sound Vib2006;293:535–46.

Nilsson M. A-weighted sound pressure level as an indicator of short-term loudness orannoyance of road-traffic sound. J Sound Vib 2007;302:197–207.

Öhrström E, Björkman M, Rylander R. Laboratory annoyance and different traffic noisesources. J Sound Vib 1980;70:333–41.

Pedersen E, LarsmanP. The impact of visual factors onnoise annoyance among people livingin the vicinity of wind turbines. J Environ Psychol 2008;28:379–89.

Pheasant R, Horoshenkov K,Watts G, Barrett B. The acoustic and visual factors influencingthe construction of tranquil space in urban and rural environments tranquil spaces—quiet places? J Acoust Soc Am 2008;123:1446–57.

Pheasant R, Fisher M, Watts G, Whitaker D, Horoshenkov K. The importance ofauditory-visual interaction in the construction of ‘tranquil space’. J Environ Psychol2010;30:501–9.

Rogge E, Nevens F, Gulinck H. Perception of rural landscapes in Flanders: lookingbeyond aesthetics. Landsc Urban Plan 2007;82:159–74.

Sakai H, Hotehama T, Ando Y, Prodi N, Pompoli R. Diagnostic system based on thehuman auditory-brain model for measuring environmental noise—an applicationto railway noise. J Sound Vib 2002;250:9–21.

Shimokura R, Soeta Y. Characteristics of train noise in above-ground and undergroundstations with side and island platforms. J Sound Vib 2011;330:1621–33.

Tamura A. Effects of landscaping on the feeling of annoyance of a space. Oldenburg,Germany: Symposium of Psychological Acoustics; 1997.

Viollon S, Lavandier C. Environmental approach of the perception of noise transmittedthrough barriers. Derborn, USA: Internoise; 2002.

Viollon S, Lavandier C, Drake C. Influence of visual setting on sound ratings in an urbanenvironment. Appl Acoust 2002;63:493–511.

Vos J. Annoyance caused by the sounds of a magnetic levitation train. J Acoust Soc Am2004;115:1597–608.

Wiens JA. Spatial scaling in ecology. Funct Ecol 1989;3:385–97.Yano T, Morihara T, Sato T. Community response to Shinkansen noise and vibration: a

survey in areas along the Sanyo Shinkansen line. Budapest, Hungary: ForumAcusticum; 2005.

You J, Jeon JY. Just noticeable differences in sound quality metrics for refrigerator noise.Noise Control Engineering Journal 2008;56:414–24.