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VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China

Passive acoustic monitoring technology is widely used to monitor the diversity of vocal animals, but the question of how to quickly extract effective sound patterns remains a challenge due to the difficulty of distinguishing biological sounds within multiple sound sources in a soundscape. In this st...

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Detalles Bibliográficos
Autores principales: Wang, Mei, Mei, Jinjuan, Darras, Kevin FA, Liu, Fanglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656901/
https://www.ncbi.nlm.nih.gov/pubmed/38025750
http://dx.doi.org/10.7717/peerj.16462
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author Wang, Mei
Mei, Jinjuan
Darras, Kevin FA
Liu, Fanglin
author_facet Wang, Mei
Mei, Jinjuan
Darras, Kevin FA
Liu, Fanglin
author_sort Wang, Mei
collection PubMed
description Passive acoustic monitoring technology is widely used to monitor the diversity of vocal animals, but the question of how to quickly extract effective sound patterns remains a challenge due to the difficulty of distinguishing biological sounds within multiple sound sources in a soundscape. In this study, we address the potential application of the VGGish model, pre-trained on Google’s AudioSet dataset, for the extraction of acoustic features, together with an unsupervised clustering method based on the Gaussian mixture model, to identify various sound sources from a soundscape of a subtropical forest in China. The results show that different biotic and abiotic components can be distinguished from various confounding sound sources. Birds and insects were the two primary biophony sound sources, and their sounds displayed distinct temporal patterns across both diurnal and monthly time frames and distinct spatial patterns in the landscape. Using the clustering and modeling method of the general sound feature set, we quickly depicted the soundscape in a subtropical forest ecosystem, which could be used to track dynamic changes in the acoustic environment and provide help for biodiversity and ecological environment monitoring.
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spelling pubmed-106569012023-11-15 VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China Wang, Mei Mei, Jinjuan Darras, Kevin FA Liu, Fanglin PeerJ Biodiversity Passive acoustic monitoring technology is widely used to monitor the diversity of vocal animals, but the question of how to quickly extract effective sound patterns remains a challenge due to the difficulty of distinguishing biological sounds within multiple sound sources in a soundscape. In this study, we address the potential application of the VGGish model, pre-trained on Google’s AudioSet dataset, for the extraction of acoustic features, together with an unsupervised clustering method based on the Gaussian mixture model, to identify various sound sources from a soundscape of a subtropical forest in China. The results show that different biotic and abiotic components can be distinguished from various confounding sound sources. Birds and insects were the two primary biophony sound sources, and their sounds displayed distinct temporal patterns across both diurnal and monthly time frames and distinct spatial patterns in the landscape. Using the clustering and modeling method of the general sound feature set, we quickly depicted the soundscape in a subtropical forest ecosystem, which could be used to track dynamic changes in the acoustic environment and provide help for biodiversity and ecological environment monitoring. PeerJ Inc. 2023-11-15 /pmc/articles/PMC10656901/ /pubmed/38025750 http://dx.doi.org/10.7717/peerj.16462 Text en © 2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biodiversity
Wang, Mei
Mei, Jinjuan
Darras, Kevin FA
Liu, Fanglin
VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China
title VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China
title_full VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China
title_fullStr VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China
title_full_unstemmed VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China
title_short VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China
title_sort vggish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern china
topic Biodiversity
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656901/
https://www.ncbi.nlm.nih.gov/pubmed/38025750
http://dx.doi.org/10.7717/peerj.16462
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