Attractive routes Sound pleasantness of pedestrian walks in urban - - PowerPoint PPT Presentation

Attractive routes

Catherine Lavandier (Pauline Delaitre – Pierre Aumond)

Sound pleasantness of pedestrian walks in urban environment

Sound pleasantness of a soundscape in urban situations

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Aim of different projects was to propose urban sound quality indicators based on perceptual and acoustic data, collected with different methods

• 60 participants (about 30 locations at different

periods)

• 100 measurements each year per participant
• Only 3409 relevant data

Cart_ASUR project (2012-2016):

• 13 perceptual data collected with mobile phones in Paris
• acoustic data collected through the mobile microphone (dB(A) 1s)

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Sound pleasantness of a soundscape in urban situations

GRAFIC project (2013-2017):

• perceptual data collected on questionnaires in Paris
• acoustic data collected through one mobile sensor (1/3 Oct band-125ms)
• georeferenced data for localisation of the measurements

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Perceptual models of sound pleasantness

Cart_ASUR (3409 individual situations) Sound pleasantness = 8.11 – 0.38 * Global loudness – 0.14 * Time ratio

• f traffic + 0.20 * Time ratio of voices + 0.15 * Time ratio of birds

(R²aj=0.34 & r = 0.58 and R²aj=0.80 & r = 0.94 for the mean values) GRAFIC (556 individual situations) Sound pleasantness = 9,70 – 0,47 * Global loudness – 0.21 * Time ratio

• f traffic + 0.12 * Time ratio of voices + 0.09 * Time ratio of birds

(R²aj=0.42 and R²aj=0,90 for the mean values)

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Perceptual variable Acoustic indicators (mean values Cart_ASUR Acoustic indicators (mean values GRAFIC) Global loudness LAeq (0,76) L50 (0,82) L90 (0,71) NNEL>70 (0,75) LAeq (0,73) L50 1Khz (0,85) L90 (0,72) N50 (0,80) Time ratio of traffic LAeq (0,63) L50 (0,62) MIL>70 (0,66) LAeq (0,66) L50 (0,76) L50 250Hz (0,83) Time ratio of voices σ (-0,61) LA10–LA90 (-0,61) TFSD 500Hz,1s (0,53) LA10–LA90 (NS) Time ratio of birds L5 (-0,55) L10 (-0,53) L10 (-0,46) TFSD 4kHz,125ms (0,81)

Acoustic models of sound pleasantness

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Sound Pleasantness = 16.92 – 0.15 * LA50 - 0,06 * (LA10-LA90) (R²aj=60%) Harmonica = 0.2*(LA95-30)+0.25*(LAeq-LA95) = - 6 + 0,25 * LAeq – 0.05 * LA95 Sound Unpleasant = -SP+11= - 5.9 + 0,15 * LA50 +0.06 * LA10 – 0.06 * LA90 Sound Pleasant. = 16.70 – 0.24 * L50 1KHz + 10.9 * TFSD 500Hz + 17.1* TFSD 4KHz (R²aj=85%)

Acoustic models of sound pleasantness

Cart_ASUR (mean values) GRAFIC (mean values) CENSE (Running project) Sound Pleasant. = ft (L50 + new indicators for presence of sources extracted from (1/3 Oct band-125ms) in real time with deep learning networks)

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Sound pleasantness of a pedestrian route

For each assessment location Additional questions about segment between the assessment locations (~1min):

• perceived pleasant. of the route
• strength and velocity of the

change of the sound environ.

dB

At the middle and the end of the route (~15min)

• perceived pleasantness of the half routes and total route

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S[1-2] S[2-3] S[3-4] S[4-5] S[5-6] S[6-7] S[7-8] S[8-9] S[9-10] ∆SP

• 0,83 -3,26* -0,61
• 0,24

2,05 0,43 1,12 0,72

• 0,62

SC 3,5 8,7 5,3 5,4 6,3 3,2 6,7 4,6 8,1 S[10-11] S[11-12] S[12-13] S[13-14] S[14-15] S[15-16] S[16-17] S[17-18] S[18-19] ∆SP

• 1,54

1,35 0,62 1,33 1,63

• 0,52 -2,84* 0,10
• 1,62

SC 6,5 5,9 5,6 4,9 8,2 2,5 8,8 6,2 8,0 SC |∆SP| Recency effect SC = perceived change over 1min GP= Global Pleasantness of the route ∆SP= difference of route pleasantness between directions

Impact of the direction of the route

Global Pleasantness = 0.85 + 0.44 * Pmean + 0.45 * Pend (R²aj=83%)

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Impact of the duration of the route

Global Pleasantness

• ver 15-minute route is

a linear function of the average of the Pleasantness of locations which were assessed during the route. (r= 0.8 R²=64%) No recency effect here,

• Only 12 mean data
• Long term memory ?

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Pleasantness in a laboratory context

Continuous assessment of the sound pleasantness At the end of the route (3-min stimuli)

• perceived pleasantness of the route
• Semi-anechoic room
• Immersive video projection
• Calibrated sound levels
• Professional sound interface
• High quality monitoring

loudspeakers

• Transaural audio restitution

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Construction of the stimuli

Controlled audio sequences 30 participants 16 paths from 2 mixed sound environments (park and boulevard) Calibrated binaural microphones Blurred fix image

Audiovisual sequences 30 participants 5 paths in both directions Recordings (3 minutes) Video – Mini camera

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Audio controlled sequences only, 3-min long

Global Pleasantness =

• 1.4 + 0.96 * Pmean + 0.22 * Pend

(R²aj=95%) (R²aj=75% if Pmean only)

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Audio visual sequences, 3 minute long

Global Pleasantness over 3-minute route is a linear function of the average of the Pleasantness of locations which were assessed during the route. (Raj²=75%). No recency effect with complex sequences.

Impact of the vision/complexity on the change in sound pleasantness

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Change of SP Saliency 21.5% Δ% = 27.2% Audio only Δ% = 14.7% Audio-Visual Saliency Change of SP 48.7% Saliency Change of SP 47.5% Change of SP Saliency 32.8%

Filipan K. et al., "Auditory sensory saliency as a better predictor of change than sound amplitude in pleasantness assessment of reproduced urban soundscapes", Building and Environment, Vol 148, pp. 730-741, (2018).

Summary

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Sound quality

• f an attractive

route 1-min walk (In Situ) 3-min experiment (Laboratory – Audio only) 3 min. (Laboratory – Audio Visual) More than a 15-min walk (In Situ) Recency effect has been shown but only for short or very controlled paths. GP = 0.85 + 0.44 * Pmean + 0.45 * Pend (R²aj=83%) GP = -1.4 + 0.96 * Pmean + 0.22 * Pend (R²aj=95%) GP = Pmean (R²aj=64%-75%) Good approximation with the average of the pleasantness of the successive locations.

Next steps – Sound Pleasantness of a route

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Next steps – CENSE Smart City Project

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• Classical acoustic

indicators out of 120 sensors in a 1 Km² area in Lorient

• Automatic extraction of

time of presence for different types of sound sources

• Automatic calculation
• f Sound Pleasantness

Sound Pleasant. = ft (L50 + new indicators for presence of source extracted from (1/3 Oct band-125ms) in real time with deep learning networks)

Next steps – Interactive maps

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• Interpolation of Sound

Pleasantness between sensors (about 50m between each sensor).

• Automatic extraction of

time of presence for

• Natural
• Human
• Traffic

sound sources, shown with dedicated signs

• n the map.

Global Sound Pleasantness of a route ? Improve the models !

Attractive routes

Catherine Lavandier (Pauline Delaitre – Pierre Aumond)

Thank you very much for your attention