%0 Journal Article
%@ 0360-1323
%A Lam, Bhan
%A Ong, Zhen-Ting
%A Ooi, Kenneth
%A Ong, Wen-Hui
%A Wong, Trevor
%A Watcharasupat, Karn N
%A Boey, Vanessa
%A Lee, Irene
%A Hong, Joo Young
%A Kang, Jian
%A Lee, Kar Fye Alvin
%A Christopoulos, Georgios
%A Gan, Woon-Seng
%D 2024
%F discovery:10198304
%I Elsevier BV
%J Building and Environment
%K Urban soundscape,   natural sounds,   auditory masking,   probabilistic approach,   soundscape augmentation,   artificial intelligence
%T Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas
%U https://discovery.ucl.ac.uk/id/eprint/10198304/
%V 266
%X 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.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.