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Analysis and Acoustic Event Classification of Environmental Data Collected in a Citizen Science Project.

Daniel Bonet-Solà¹, Ester Vidaña-Vila¹, Rosa Ma Alsina-Pagès¹

¹Human Environment Research (HER), La Salle-Universitat Ramon Llull, Sant Joan de La Salle, 42, 08022 Barcelona, Spain.

International Journal of Environmental Research and Public Health

|February 25, 2023

Summary

Citizen science projects like Sons al Balcó monitor soundscapes. A new tool using a convolutional neural network automatically detects sound events, aiding soundscape quality assessment.

Keywords:

acoustic event detection citizen science convolutional neural networks noise annoyance

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Area of Science:

Environmental acoustics
Citizen science
Bioacoustics

Background:

Citizen science offers valuable data for monitoring environmental changes, including soundscapes.
Processing large datasets from citizen science initiatives presents significant challenges.
Understanding soundscape dynamics is crucial for assessing environmental quality.

Purpose of the Study:

To investigate soundscape changes in Catalonia during and after the COVID-19 lockdown using citizen science data.
To develop and evaluate an automated tool for detecting and classifying sound events.
To compare data from two citizen science data collection campaigns.

Main Methods:

Collected acoustic data through the "Sons al Balcó" citizen science project in 2020 and 2021.
Trained a convolutional neural network (CNN) for automatic detection and classification of acoustic events.
Analyzed the performance of the CNN using event-based macro F1-score.

Main Results:

The CNN achieved an event-based macro F1-score above 50% for prevalent noise sources in both campaigns.
The automated tool demonstrated effectiveness in detecting and classifying acoustic events, even simultaneous ones.
Detection accuracy varied, influenced by event prevalence and foreground-to-background ratio.

Conclusions:

Citizen science, coupled with automated analysis tools, can effectively monitor soundscape changes.
The developed CNN provides a promising first step towards automated soundscape quality assessment.
Further refinement is needed to improve detection rates for less prevalent or lower-ratio acoustic events.