Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas
<|reference_start|>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 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.<|reference_end|>
@article{lam2024automating,
    title={Automating Urban Soundscape Enhancements with AI: In-situ Assessment of
  Quality and Restorativeness in Traffic-Exposed Residential Areas},
    author={Bhan Lam, Zhen-Ting Ong, Kenneth Ooi, Wen-Hui Ong, Trevor Wong, Karn
  N. Watcharasupat, Vanessa Boey, Irene Lee, Joo Young Hong, Jian Kang, Kar Fye
  Alvin Lee, Georgios Christopoulos, and Woon-Seng Gan},
    journal={Building and Environment, vol. 266, p. 112106, Dec. 2024},
    year={2024},
    doi={10.1016/j.buildenv.2024.112106},
    archivePrefix={arXiv},
    eprint={2407.05744},
    primaryClass={eess.AS cs.SD}
}