%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.
%K Urban soundscape, 
natural sounds, 
auditory masking, 
probabilistic approach, 
soundscape augmentation, 
artificial intelligence
%V 266
%A Bhan Lam
%A Zhen-Ting Ong
%A Kenneth Ooi
%A Wen-Hui Ong
%A Trevor Wong
%A Karn N Watcharasupat
%A Vanessa Boey
%A Irene Lee
%A Joo Young Hong
%A Jian Kang
%A Kar Fye Alvin Lee
%A Georgios Christopoulos
%A Woon-Seng Gan
%J Building and Environment
%I Elsevier BV
%L discovery10198304
%D 2024
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%T Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas