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

Daniel Bonet-Solà, Ester Vidaña-Vila () and Rosa Ma Alsina-Pagès ()
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Daniel Bonet-Solà: Human Environment Research (HER), La Salle—Universitat Ramon Llull, Sant Joan de La Salle, 42, 08022 Barcelona, Spain
Ester Vidaña-Vila: Human Environment Research (HER), La Salle—Universitat Ramon Llull, Sant Joan de La Salle, 42, 08022 Barcelona, Spain
Rosa Ma Alsina-Pagès: Human Environment Research (HER), La Salle—Universitat Ramon Llull, Sant Joan de La Salle, 42, 08022 Barcelona, Spain

IJERPH, 2023, vol. 20, issue 4, 1-23

Abstract: Citizen science can serve as a tool to obtain information about changes in the soundscape. One of the challenges of citizen science projects is the processing of data gathered by the citizens, to obtain conclusions. As part of the project Sons al Balcó , authors aim to study the soundscape in Catalonia during the lockdown due to the COVID-19 pandemic and afterwards and design a tool to automatically detect sound events as a first step to assess the quality of the soundscape. This paper details and compares the acoustic samples of the two collecting campaigns of the Sons al Balcó project. While the 2020 campaign obtained 365 videos, the 2021 campaign obtained 237. Later, a convolutional neural network is trained to automatically detect and classify acoustic events even if they occur simultaneously. Event based macro F1-score tops 50% for both campaigns for the most prevalent noise sources. However, results suggest that not all the categories are equally detected: the percentage of prevalence of an event in the dataset and its foregound-to-background ratio play a decisive role.

Keywords: citizen science; acoustic event detection; noise annoyance; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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