Replication Data for: Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas

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Lam, Bhan; Ong, Zhen-Ting; Ooi, Kenneth; Boey, Vanessa; Lee, Irene; Gan, Woon-Seng, 2024, "Replication Data for: Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas", https://doi.org/10.21979/N9/NEH5TR, DR-NTU (Data), V1, UNF:6:9KOXblQ6z2TXvHhC2clk1w== [fileUNF]
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Description

This dataset contains the following data tables:

  • amss_insitu_participant_data.csv: Participant responses to site evaluation
  • comb_obj_insitu_session_data.csv: Per session combined objective acoustic measurement and subjective response data

The variables contained in each data table are detailed below:

  • amss_insitu_participant_data.csv
    • pID: [numeric] Unique participant identification
    • site: [Factor w/ 3 levels "GND", "ROOF", "MP"] Variable describing the site, where "GND" refers to the pavilion on the ground floor, "ROOF" is the pavilion in the rooftop garden, and "MP" is the meeting point
    • condition: [Factor w/ 2 levels "AMB", "AMSS"] Variable describing the condition
    • order: [Factor w/ 2 levels "18", "81"] Variable describing the order in which the participant assesses the evaluation sites. "18" indicates that a participant evaluated the soundscape at "GND" first followed by "ROOF" and vice versa for "81".
    • partGrp: [Factor w/ 2 levels "multi", "single"] Variable describing the number of participants in the session for each participant, where "single" indicates that the participant conducted the experiment alone and "multi" indicates that more than one person was present during
    • dom_noise: [numeric] Normalised score for the dominance of noise sources (DOMNoi)
    • dom_human: [numeric] Normalised score for the dominance of human sound sources (DOMHum)
    • dom_natural: [numeric] Normalised score for the dominance of natural sound sources (DOMNat)
    • p: [numeric] Normalised score for the pleasant attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (rp)
    • e: [numeric] Normalised score for the eventful attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (re)
    • ch: [numeric] Normalised score for the chaotic attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (rch)
    • v: [numeric] Normalised score for the vibrant attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (rv)
    • u: [numeric] Normalised score for the uneventful attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (ru)
    • ca: [numeric] Normalised score for the calm attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (rca)
    • a: [numeric] Normalised score for the annoying attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (ra)
    • m: [numeric] Normalised score for the monotonous attribute in the perceived affective quality scale as per Method A questionnaire in ISO 12913-2 (rm)
    • overall: [numeric] Normalised score for the overall quality of the acoustic environment as per Method A questionnaire in ISO 12913-2 (OSQ)
    • appropriate: [numeric] Normalised score for the appropriateness of the acoustic environment as per Method A questionnaire in ISO 12913-2 (APPR)
    • loudness: [numeric] Normalised score for the overall loudness of the acoustic environment (PLN)
    • ISOPL: [numeric] Derived normalised score for the ``Pleasantness'' score calculated using equation A.1 in ISO 12913-3 (ISOPL)
    • ISOEV: [numeric] Derived normalised score for the ``Eventfulness'' score calculated using equation A.1 in ISO 12913-3 (ISOEV)
    • PRSSFas: [numeric] Derived normalised score for the ``Fascination'' dimension in the Perceived Restorativeness Soundscape Scale (PRSSFas)
    • PRSSBA: [numeric] Derived normalised score for the ``Being-Away'' dimension in the Perceived Restorativeness Soundscape Scale (PRSSBA)
    • PRSSCom: [numeric] Derived normalised score for the ``Compatibility'' dimension in the Perceived Restorativeness Soundscape Scale (PRSSCom)
    • PRSSEC: [numeric] Derived normalised score for the ``Extent-Coherence'' dimension in the Perceived Restorativeness Soundscape Scale (PRSSEC)
    • PRSSES: [numeric] Derived normalised score for the ``Extent-Scope'' dimension in the Perceived Restorativeness Soundscape Scale (PRSSES)
    • PosAff: [numeric] Derived normalised score for positive affect in the I-PANAS-SF scale (PA)
    • NegAff: [numeric] Derived normalised score for negative affect in the I-PANAS-SF scale (NA)
    • pss: [numeric] Derived normalised score for the 10-item perceived stress scale (PSS-10)
    • wnss: [numeric] Derived normalised score for the 21-item Weinstein noise sensitivity scale (INS)
    • wbi: [numeric] Derived normalised score for the 5-item WHO well-being index (WHO-5)
    • panas_*: [numeric] Individual raw ratings of the I-PANAS-SF questionnaire
    • annoy-*: [numeric] Individual raw ratings of the ISO 15666 questionnaire on annoyance
    • gender: [Factor w/ 2 levels "Male", "Female"] reported gender
    • occupation: [chr] reported occupation
    • occupation_other: [chr] self-reported occupation for "other" category
    • age: [numeric] reported age
    • pss*: [numeric] Individual raw ratings of the Perceived Stress Scale questionnaire
    • wnss*: [numeric] Individual raw ratings of the Weinstein Noise Sensitivity Scale questionnaire
    • who*: [numeric] Individual raw ratings of the WHO Well-being Index questionnaire
  • comb_obj_insitu_session_data.csv
    • date: [character] Date in YYYYMMDD format
    • sessionTime: [numeric] The hour of date when the evaluation session started
    • site: [Factor w/ 2 levels "GND", "ROOF"] Variable describing the site
    • condition: [Factor w/ 2 levels "AMB", "AMSS"] Variable describing the condition
    • L[Aeq]: [numeric] The C-weighted equivalent sound pressure level for 10-min listening period of the session at date and sessionTime
    • L[Ceq]: [numeric] The C-weighted equivalent sound pressure level for 10-min listening period of the session at date and sessionTime
    • N[95]: [numeric] The 95 % exceedance levels of psychoacoustic loudness for 10-min listening period of the session at date and sessionTime
    • pair: [numeric] A combination of condition--site for plotting purposes
    • attribute: [numeric] The collated subjective attributes of ISOPL, OSQ, PRSSFas, PRSSBA, and PRSSCom
    • score: [numeric] The hour of date when the evaluation session started
  • enviro_session_data.csv
    • startDate: [POSIXlt] The local POSIXlt datetime indicating the start of the 10-min listening period of the session
    • endDate: [POSIXlt] The local POSIXlt datetime indicating the end of the 10-min listening period of the session
    • condition: [Factor w/ 2 levels "AMB", "AMSS"] Variable describing the condition
    • temperature: [numeric] Variable describing the temperature in °C
    • humidity: [numeric] Variable describing the humidity in %RH
    • lux: [numeric] Variable describing the luminance in lux
    • windspeed: [numeric] Variable describing the wind speed in km/h
    • 24h_psi: [numeric] Variable describing the 24 hour Pollution Standards Index
    • pm25: [numeric] Variable describing the 1-hour PM2.5 reading in µg/m3
  • predict_session_data.csv
    • timestamp: [POSIXlt] The local POSIXlt datetime indicating the time at which the 30-s long masker was played at the ROOF pavilion
    • predictions: [numeric] Variable describing the masker that was chosen based on the highest predicted ISOPL increase
    • condition: [Factor w/ 1 level "AMSS"] Variable describing the condition
  • fullData.RData
    • amss_insitu_participant_data: [data.frame] R S3 tibble data.frame containing the data in amss_insitu_participant_data.csv
    • comb_obj_insitu_session_data: [data.frame] R S3 tibble data.frame containing the data in comb_obj_insitu_session_data.csv
    • enviro_session_data: [data.frame] R S3 tibble data.frame containing the data in enviro_session_data.csv
    • predict_session_data: [data.frame] R S3 tibble data.frame containing the data in predict_session_data.csv
(2024-05-04)
Subject Arts and Humanities; Computer and Information Science; Engineering
Keyword Soundscape, Artificial Intelligence
Related Publication Lam, B., Ong, Z. T., Ooi, K., Ong, W. H., Wong, T., Watcharasupat, K. N., ... & Gan, W. S. (2024). Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas. arXiv preprint arXiv:2407.05744. doi: 10.48550/arXiv.2407.05744
Grant Information
National Research Foundation (NRF): COT-V4-2020-1
Ministry of National Development (MND): COT-V4-2020-1
License/Data Use Agreement

CC BY-NC-ND 4.0

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amss_insitu_participant_data.tab
Tabular Data - 55.0 KB
Published Jul 6, 2024
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90 Variables, 205 Observations UNF:6:52pESJGuyfEQILbc8nImyQ==
Participant responses to site evaluation questionnaire and participant information questionnaire
Preview "amss_insitu_participant_data.tab"
comb_obj_insitu_session_data.tab
Tabular Data - 33.3 KB
Published Jul 6, 2024
2 Downloads
10 Variables, 440 Observations UNF:6:auze+RNDNLrv5GvfQpfEWg==
Per session combined objective acoustic measurement and subjective response data
Preview "comb_obj_insitu_session_data.tab"
enviro_session_data.tab
Tabular Data - 4.2 KB
Published Jul 6, 2024
2 Downloads
9 Variables, 68 Observations UNF:6:DLP7Ahd3xy0l7c3DDjEBew==
Environmental data logged during the 10-min listening period per session
Preview "enviro_session_data.tab"
fullData.RData
R Data - 21.0 KB
Published Jul 6, 2024
14 Downloads
MD5: 9cb65580c64235ae0119048ad9646203
RData file containing all the csv files in this repository as individual dataframes
predict_session_data.tab
Tabular Data - 19.3 KB
Published Jul 6, 2024
1 Download
3 Variables, 481 Observations UNF:6:bFsD8nvT4ywfzYFWDh7zqA==
Predicted bird maskers logged during the 10-min listening period per session
Preview "predict_session_data.tab"
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