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Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas

Lam, Bhan; Ong, Zhen-Ting; Ooi, Kenneth; Ong, Wen-Hui; Wong, Trevor; Watcharasupat, Karn N; Boey, Vanessa; ... Gan, Woon-Seng; + view all (2024) Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas. Building and Environment , 266 , Article 112106. 10.1016/j.buildenv.2024.112106.

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Abstract

Formalized in ISO 12913, the “soundscape” approach is a paradigmatic shift towards perceptionbased 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 (푁 = 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 퐿A,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.

Type: Article
Title: Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas
DOI: 10.1016/j.buildenv.2024.112106
Publisher version: https://doi.org/10.1016/j.buildenv.2024.112106
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Urban soundscape, natural sounds, auditory masking, probabilistic approach, soundscape augmentation, artificial intelligence
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10198304
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