1. Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings
- Author
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Francesco Aletta, Francesco Babich, Stefano Siboni, Simone Torresin, Tin Oberman, Rossano Albatici, and Jian Kang
- Subjects
Soundscape ,Environmental Engineering ,Computer science ,media_common.quotation_subject ,Geography, Planning and Development ,0211 other engineering and technologies ,02 engineering and technology ,Acoustic comfort ,010501 environmental sciences ,Space (commercial competition) ,Machine learning ,computer.software_genre ,01 natural sciences ,Loudness ,Perception ,11. Sustainability ,Quality (business) ,021108 energy ,0105 earth and related environmental sciences ,Civil and Structural Engineering ,media_common ,Acoustic perception ,Indoor environmental quality ,Indoor soundscape ,Residential ,business.industry ,Building and Construction ,Sight ,Principal component analysis ,Artificial intelligence ,business ,computer ,Meaning (linguistics) - Abstract
Models of perceived affective quality of soundscapes have been recently included into standards to guide the measurement and improvement of urban soundscapes. Such models have been developed in outdoor contexts and their validity in indoor built environments is unclear. A laboratory listening test was performed in a mock-up living room with a window sight, in order to develop an indoor soundscape model for residential buildings. During the test, 35 participants were asked to rate 20 different scenarios each. Scenarios were defined by combining four indoor sound sources and five urban environments, filtered through a window ajar, on 97 attribute scales. By applying principal component analysis, Comfort, Content, and Familiarity, were extracted as the main perceptual dimensions explaining respectively 58%, 25% and 7% of the total variance. Relationships between the principal component scores, acoustic parameters and indoor and outdoor sound categories were investigated. Comfort, Content, and Familiarity were found to be better predicted respectively by loudness N10, level variability LA10-LA90 and sharpness S. The magnitude of linear-mixed-effect model predictions sensibly improved by accounting for sound categories, thus pointing at the importance of semantic meaning of sounds in indoor soundscape assessment. A measurement system is proposed, based on a 2-D space defined by two orthogonal axes, Comfort and Content, and two additional axes, Engagement and Privacy – Control, rotated 45° on the same plane. The model indicates the perceptual constructs to be measured (e.g. in post-occupancy evaluations), the attribute scales to be employed and actions to improve indoor soundscape quality, thus providing a reference for both research and practice.
- Published
- 2020
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