eprintid: 10198304
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/83/04
datestamp: 2024-10-10 10:32:10
lastmod: 2024-10-10 10:32:33
status_changed: 2024-10-10 10:32:10
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Lam, Bhan
creators_name: Ong, Zhen-Ting
creators_name: Ooi, Kenneth
creators_name: Ong, Wen-Hui
creators_name: Wong, Trevor
creators_name: Watcharasupat, Karn N
creators_name: Boey, Vanessa
creators_name: Lee, Irene
creators_name: Hong, Joo Young
creators_name: Kang, Jian
creators_name: Lee, Kar Fye Alvin
creators_name: Christopoulos, Georgios
creators_name: Gan, Woon-Seng
title: Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas
ispublished: pub
divisions: UCL
divisions: B04
divisions: C04
divisions: F34
keywords: Urban soundscape, 
natural sounds, 
auditory masking, 
probabilistic approach, 
soundscape augmentation, 
artificial intelligence
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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 (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.
date: 2024-12-01
date_type: published
publisher: Elsevier BV
official_url: https://doi.org/10.1016/j.buildenv.2024.112106
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2323886
doi: 10.1016/j.buildenv.2024.112106
lyricists_name: Kang, Jian
lyricists_id: JKANG71
actors_name: Kang, Jian
actors_id: JKANG71
actors_role: owner
full_text_status: restricted
publication: Building and Environment
volume: 266
article_number: 112106
issn: 0360-1323
citation:        Lam, Bhan;    Ong, Zhen-Ting;    Ooi, Kenneth;    Ong, Wen-Hui;    Wong, Trevor;    Watcharasupat, Karn N;    Boey, Vanessa;                         ... Gan, Woon-Seng; + view all <#>        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;   - view fewer <#>    (2024)    Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas.                   Building and Environment , 266     , Article 112106.  10.1016/j.buildenv.2024.112106 <https://doi.org/10.1016/j.buildenv.2024.112106>.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10198304/1/BAE_2024.112106_accepted_preprint.pdf