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

Formalized in ISO 12913, the "soundscape" approach is a paradigmatic shift towards perception-based urban sound management, aiming to alleviate the substantial socioeconomic costs of noise pollution to advance the United Nations Sustainable Development Goals. Focusing on traffic-exposed ou...

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Veröffentlicht in:arXiv.org 2024-10
Hauptverfasser: Bhan Lam, Zhen-Ting Ong, Ooi, Kenneth, Wen-Hui, Ong, Wong, Trevor, Watcharasupat, Karn N, Boey, Vanessa, Lee, Irene, Joo Young Hong, Kang, Jian, Kar Fye Alvin Lee, Christopoulos, Georgios, Woon-Seng Gan
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Sprache:eng
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Zusammenfassung:Formalized in ISO 12913, the "soundscape" approach is a paradigmatic shift towards perception-based urban sound management, aiming to alleviate the substantial socioeconomic costs of noise pollution to advance the United Nations Sustainable Development Goals. Focusing on traffic-exposed outdoor residential sites, we implemented an automatic masker selection system (AMSS) utilizing natural sounds to mask (or augment) traffic soundscapes. We employed a pre-trained AI model to automatically select the optimal masker and adjust its playback level, adapting to changes over time in the ambient environment to maximize "Pleasantness", a perceptual dimension of soundscape quality in ISO 12913. Our validation study involving (\(N=68\)) residents revealed a significant 14.6 % enhancement in "Pleasantness" after intervention, correlating with increased restorativeness and positive affect. Perceptual enhancements at the traffic-exposed site matched those at a quieter control site with 6 dB(A) lower \(L_\text{A,eq}\) and road traffic noise dominance, affirming the efficacy of AMSS as a soundscape intervention, while streamlining the labour-intensive assessment of "Pleasantness" with probabilistic AI prediction.
ISSN:2331-8422
DOI:10.48550/arxiv.2407.05744