Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data

Abstract:

The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.

Content:

• 01 StudyDescription.pdf
  • Description of the field study.
  • Information about the methods and materials used.
• 02 Dataset.csv 
  • The dataset, consisting of 100 variables describing 6594 observations taken by 105 participants.
• 03 VariableDescriptions_EnglishPersonQuestionnaire.pdf
  • Descriptions of all variables, their measurement scale, scale ranges and levels.
  • Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.
  • English translations of questions asked in the person questionnaire.
• 04 ESM-Questionnaire.pdf 
  • Screenshots of the original Experience Sampling Method questionnaire with English translations.
• 05 PersonQuestionnaire_OriginalGermanVersion.pdf 
  • Original version of the person questionnaire in German language.
• 06 HelpTexts.pdf 
  • Descriptions of the study task.
  • Explanations of the scales used in the questionnaire.
  • Explanations of the sound categories and the soundscape composition.
  • Explanation of the operation of the recording device.
• TimeSeries_and_Spectrograms_README.md
  • Detailed description of the JASON and MAT files, how they were created and how to import them.
• TimeSeries_and_Spectrograms_JSON.zip 
  • Open file format containing....
  • L_Aeq time series of the binaural recordings, averaging periods of 2 ms each.
  • Third-octave spectrograms (unweighted and A-weighted) every 23 ms.
• TimeSeries_and_Spectrograms_MAT.zip 
  • MATLAB (R) file format containing....
  • L_Aeq time series of the binaural recordings, averaging periods of 2 ms each.
  • Third-octave spectrograms (unweighted and A-weighted) every 23 ms.

Publications refering to this dataset:

Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."

Funding:

This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. 

License:

CC 4.0 BY, https://creativecommons.org/licenses/by/4.0/legalcode

Version history:

•  V.01.0. March 7, 2023: Initial publication.