Detecting, classifying, and counting blue whale calls with Siamese neural networks
The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the under...
Published in: | The Journal of the Acoustical Society of America |
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Online Access: | https://archimer.ifremer.fr/doc/00693/80505/83708.pdf https://doi.org/10.1121/10.0004828 https://archimer.ifremer.fr/doc/00693/80505/ |
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ftarchimer:oai:archimer.ifremer.fr:80505 2023-05-15T15:45:07+02:00 Detecting, classifying, and counting blue whale calls with Siamese neural networks Zhong, Ming Torterotot, Maelle Branch, Trevor A. Stafford, Kathleen M. Royer, Jean-yves Dodhia, Rahul Lavista Ferres, Juan 2021-05 application/pdf https://archimer.ifremer.fr/doc/00693/80505/83708.pdf https://doi.org/10.1121/10.0004828 https://archimer.ifremer.fr/doc/00693/80505/ eng eng Acoustical Society of America (ASA) https://archimer.ifremer.fr/doc/00693/80505/83708.pdf doi:10.1121/10.0004828 https://archimer.ifremer.fr/doc/00693/80505/ info:eu-repo/semantics/openAccess restricted use Journal Of The Acoustical Society Of America (0001-4966) (Acoustical Society of America (ASA)), 2021-05 , Vol. 149 , N. 5 , P. 3086-3094 text Publication info:eu-repo/semantics/article 2021 ftarchimer https://doi.org/10.1121/10.0004828 2022-02-22T23:50:59Z The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%–6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls. Article in Journal/Newspaper Blue whale Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) Indian The Journal of the Acoustical Society of America 149 5 3086 3094 |
institution |
Open Polar |
collection |
Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) |
op_collection_id |
ftarchimer |
language |
English |
description |
The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%–6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls. |
format |
Article in Journal/Newspaper |
author |
Zhong, Ming Torterotot, Maelle Branch, Trevor A. Stafford, Kathleen M. Royer, Jean-yves Dodhia, Rahul Lavista Ferres, Juan |
spellingShingle |
Zhong, Ming Torterotot, Maelle Branch, Trevor A. Stafford, Kathleen M. Royer, Jean-yves Dodhia, Rahul Lavista Ferres, Juan Detecting, classifying, and counting blue whale calls with Siamese neural networks |
author_facet |
Zhong, Ming Torterotot, Maelle Branch, Trevor A. Stafford, Kathleen M. Royer, Jean-yves Dodhia, Rahul Lavista Ferres, Juan |
author_sort |
Zhong, Ming |
title |
Detecting, classifying, and counting blue whale calls with Siamese neural networks |
title_short |
Detecting, classifying, and counting blue whale calls with Siamese neural networks |
title_full |
Detecting, classifying, and counting blue whale calls with Siamese neural networks |
title_fullStr |
Detecting, classifying, and counting blue whale calls with Siamese neural networks |
title_full_unstemmed |
Detecting, classifying, and counting blue whale calls with Siamese neural networks |
title_sort |
detecting, classifying, and counting blue whale calls with siamese neural networks |
publisher |
Acoustical Society of America (ASA) |
publishDate |
2021 |
url |
https://archimer.ifremer.fr/doc/00693/80505/83708.pdf https://doi.org/10.1121/10.0004828 https://archimer.ifremer.fr/doc/00693/80505/ |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Blue whale |
genre_facet |
Blue whale |
op_source |
Journal Of The Acoustical Society Of America (0001-4966) (Acoustical Society of America (ASA)), 2021-05 , Vol. 149 , N. 5 , P. 3086-3094 |
op_relation |
https://archimer.ifremer.fr/doc/00693/80505/83708.pdf doi:10.1121/10.0004828 https://archimer.ifremer.fr/doc/00693/80505/ |
op_rights |
info:eu-repo/semantics/openAccess restricted use |
op_doi |
https://doi.org/10.1121/10.0004828 |
container_title |
The Journal of the Acoustical Society of America |
container_volume |
149 |
container_issue |
5 |
container_start_page |
3086 |
op_container_end_page |
3094 |
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