Pregled bibliografske jedinice broj: 1162326
Development of a multi-level predictive soundscape model to assess the soundscapes of public spaces during the COVID-19 lockdowns
Development of a multi-level predictive soundscape model to assess the soundscapes of public spaces during the COVID-19 lockdowns // The Journal of the Acoustical Society of America, 150(4)
Seattle (WA), Sjedinjene Američke Države: Acoustical Society of America (ASA), 2021. A293, 1 doi:10.1121/10.0008334 (predavanje, međunarodna recenzija, sažetak, znanstveni)
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Naslov
Development of a multi-level predictive soundscape model to assess the soundscapes of public spaces during the COVID-19 lockdowns
Autori
Mitchell, Andrew ; Oberman, Tin ; Aletta, Francesco ; Kang, Jian
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
The Journal of the Acoustical Society of America, 150(4)
/ - : Acoustical Society of America (ASA), 2021
Skup
181st Meeting of the Acoustical Society of America
Mjesto i datum
Seattle (WA), Sjedinjene Američke Države, 29.11.2021. - 03.12.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
soundscape ; COVID-19 ; lockdown ; multi-level model ; public space
Sažetak
The recent developments in the standardization of soundscape as both a research and engineering field have highlighted the need for models which can predict likely soundscape assessment from objective measurements. Such a need was highlighted during the COVID-19 lockdowns. The unprecedented restrictions in human activity presented a unique opportunity to investigate the urban noise impacts of drastic reductions in traffic noise and human sounds, but simultaneously made it impossible to carry out standard methods of soundscape assessment (i.e., in-person surveys or soundwalks). To address this, a multi-level linear regression model was developed based on an existing database of soundscape surveys and binaural recordings to predict how the soundscapes of 13 locations in London and Venice would have likely been perceived during the lockdowns based on objective measurements. To build this model, a feature selection process was applied to an extended suite of psychoacoustic metrics and a variable characterising the context of each location to identify a minimum set of input features and model structure. This presentation will demonstrate the development of this model, its application in the COVID case study and corresponding results, and will discuss the potential for future applications of a similar predictive soundscape modelling framework.
Izvorni jezik
Engleski
Znanstvena područja
Arhitektura i urbanizam, Interdisciplinarne tehničke znanosti
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
- MEDLINE