A retrospective on monitoring noise pollution with machine learning in the Sounds of New York City project

Abstract

The Sounds of New York City (SONYC) project (2016–2022) was a project to monitor and mitigate urban noise pollution using a smart acoustic sensor network, citizen scientists, and collaboration with city agencies. During its lifetime, the project deployed 75 fixed-location sensors and collected over 150 × 106 10-s audio recordings. A key element of the project was the development of machine listening models to detect the sources of noise pollution rather than just the overall noise level. In this talk, we first discuss our initial approach to data collection and machine listening including sensor development and deployment, citizen-science data annotation, self-supervised audio representation learning, and downstream sound-event detection model training. We then discuss analysis results using the outputs of these models, followed by the challenges and limitations of our initial approach. Finally, we discuss our solutions to overcome those challenges, such as citizen-deployed sensors, source-specific loudness estimation, and few-shot sound-event detection.