Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929535186894848 |
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| author | Lam, Bhan Ong, Zhen-Ting Ooi, Kenneth Ong, Wen-Hui Wong, Trevor Watcharasupat, Karn N. Boey, Vanessa Lee, Irene Hong, Joo Young Kang, Jian Lee, Kar Fye Alvin Christopoulos, Georgios Gan, Woon-Seng |
| author_facet | Lam, Bhan Ong, Zhen-Ting Ooi, Kenneth Ong, Wen-Hui Wong, Trevor Watcharasupat, Karn N. Boey, Vanessa Lee, Irene Hong, Joo Young Kang, Jian Lee, Kar Fye Alvin Christopoulos, Georgios Gan, Woon-Seng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05744 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Lee, Irene Hong, Joo Young Kang, Jian Lee, Kar Fye Alvin Christopoulos, Georgios Gan, Woon-Seng Audio and Speech Processing Sound 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. |
| title | Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2407.05744 |