Developing a deep learning-based noise annoyance prediction tool for urban environments in Singapore

Abstract

In this study, we develop a noise annoyance prediction tool using deep learning in Singapore, a densely populated city-state where a significant portion of the population resides in public housing near various noise sources like MRT, highways, bus routes, and construction sites. To investigate short-term annoyance caused by surrounding noises, we created an easily accessible web-based subjective listening test. We created a noise dataset featuring typical Singaporean noise sources, including traffic (buses, highways), trains, aviation, neighbourhood activities (playgrounds, schools, hawker centers, funerals, wildlife, home renovations), and construction. Participants were exposed to 35 noise stimuli, lasting between 15 to 30 seconds, and asked to rate perceived annoyance on a 5-point scale, with 5 being the most annoying. Using these reported annoyance levels, we ranked the stimuli relative to each other and categorized them into three classes: low, medium, and high noise annoyance. Using these three labels, Long-Short Term Memory (LSTM) networks were trained to predict the perceived annoyance of new audio samples. Such a perceived annoyance assessment tool for new audio samples can help in addressing specific noise concerns of residents, leading to more effective and targeted noise control strategies, and consequently creating a quieter and more pleasant urban living environment.