@article{discovery10198304,
       publisher = {Elsevier BV},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          volume = {266},
           month = {December},
           title = {Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas},
            year = {2024},
         journal = {Building and Environment},
        keywords = {Urban soundscape, 
natural sounds, 
auditory masking, 
probabilistic approach, 
soundscape augmentation, 
artificial intelligence},
        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 (? =
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 ?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.},
             url = {https://doi.org/10.1016/j.buildenv.2024.112106},
          author = {Lam, Bhan and Ong, Zhen-Ting and Ooi, Kenneth and Ong, Wen-Hui and Wong, Trevor and Watcharasupat, Karn N and Boey, Vanessa and Lee, Irene and Hong, Joo Young and Kang, Jian and Lee, Kar Fye Alvin and Christopoulos, Georgios and Gan, Woon-Seng},
            issn = {0360-1323}
}