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Soundscape Characterization Using Autoencoders and Unsupervised Learning

Authors :
Daniel Alexis Nieto-Mora
Maria Cristina Ferreira de Oliveira
Camilo Sanchez-Giraldo
Leonardo Duque-Muñoz
Claudia Isaza-Narváez
Juan David Martínez-Vargas
Source :
Sensors, Vol 24, Iss 8, p 2597 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area’s soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

Details

Language :
English
ISSN :
24082597 and 14248220
Volume :
24
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.45a52b681caa4ca0b7c4c3353245e135
Document Type :
article
Full Text :
https://doi.org/10.3390/s24082597