Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas

TL;DR

This paper presents an AI-driven system that automatically selects and adjusts natural sound maskers to improve the perceived quality and restorativeness of urban soundscapes in traffic-heavy residential areas, validated by resident feedback.

Contribution

It introduces an AI-based automatic masker selection system (AMSS) that dynamically enhances urban soundscapes, streamlining assessment and improving perceptual quality in traffic-exposed environments.

Findings

14.6% increase in perceived Pleasantness after intervention

AMSS achieves comparable perceptual improvements to quieter control sites

Streamlines soundscape assessment with probabilistic AI prediction

Abstract

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.