Application of Deep Learning to Enforce Environmental Noise Regulation in an Urban Setting

Vicente Carrasco, Jorge P. Arenas, Pablo Huijse, Diego Espejo, Victor Vargas, Rhoddy Viveros-Muñoz, Victor Poblete, Matthieu Vernier and Enrique Suárez ()
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Vicente Carrasco: Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Jorge P. Arenas: Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Pablo Huijse: Instituto de Informática, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Diego Espejo: Instituto de Informática, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Victor Vargas: Instituto de Informática, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Rhoddy Viveros-Muñoz: Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Victor Poblete: Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Matthieu Vernier: Instituto de Informática, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile
Enrique Suárez: Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5110701, Chile

Sustainability, 2023, vol. 15, issue 4, 1-15

Abstract: Reducing environmental noise in urban settings, i.e., unwanted or harmful outdoor sounds produced by human activity, has become an important issue in recent years. Most countries have established regulations that set maximum permitted noise levels. However, enforcing these regulations effectively remains challenging as it requires active monitoring networks and audio analysis performed by trained specialists. The manual evaluation of the audio recordings is laborious, time-consuming, and inefficient since many audios exceeding the noise level threshold do not correspond to a sound event considered by the regulation. To address this challenge, this work proposes a computational pipeline to assist specialists in detecting noise sources in the built environment that do not comply with the Chilean noise regulation. The system incorporates a deep neural model following a pre-trained audio neural network architecture transferred to a dataset compiled from public sources and recordings in Valdivia, Chile. The target dataset follows a customized taxonomy of urban sound events. The system also uses a public API so potential users can post audio files to obtain a prediction matrix reporting the presence of noise sources contributing to environmental noise pollution. Experiments using recordings from two continuous noise monitoring stations showed that the amount of data to be inspected by the specialist is decreased by 97% when the deep-learning tools are used. Therefore, this system efficiently assists trained experts in enforcing noise legislation through machine-assisted environmental noise monitoring.

Keywords: environmental noise; urban noise regulation; artificial neural networks; deep-learning; audio tagging (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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