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A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale

In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whal...

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Detalles Bibliográficos
Autor principal: Sattar, Farook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056851/
https://www.ncbi.nlm.nih.gov/pubmed/36991759
http://dx.doi.org/10.3390/s23063048
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author Sattar, Farook
author_facet Sattar, Farook
author_sort Sattar, Farook
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description In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whale calls quite precisely in the increasingly noisy ocean with a small dataset. The performances shown in terms of classification accuracy (>97%) demonstrate the efficiency of the proposed method which outperforms the relevant state-of-the-art methods. In this way, passive acoustic technology can be enhanced to monitor endangered whale calls. Efficient tracking of their numbers, migration paths and habitat become vital to whale conservation by lowering the number of preventable injuries and deaths while making progress in their recovery.
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spelling pubmed-100568512023-03-30 A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale Sattar, Farook Sensors (Basel) Article In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whale calls quite precisely in the increasingly noisy ocean with a small dataset. The performances shown in terms of classification accuracy (>97%) demonstrate the efficiency of the proposed method which outperforms the relevant state-of-the-art methods. In this way, passive acoustic technology can be enhanced to monitor endangered whale calls. Efficient tracking of their numbers, migration paths and habitat become vital to whale conservation by lowering the number of preventable injuries and deaths while making progress in their recovery. MDPI 2023-03-12 /pmc/articles/PMC10056851/ /pubmed/36991759 http://dx.doi.org/10.3390/s23063048 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sattar, Farook
A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
title A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
title_full A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
title_fullStr A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
title_full_unstemmed A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
title_short A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
title_sort new acoustical autonomous method for identifying endangered whale calls: a case study of blue whale and fin whale
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056851/
https://www.ncbi.nlm.nih.gov/pubmed/36991759
http://dx.doi.org/10.3390/s23063048
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