Deep neural networks for automated detection of marine mammal species
Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks...
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ftpubmed:oai:pubmedcentral.nih.gov:6969184 2023-05-15T16:08:18+02:00 Deep neural networks for automated detection of marine mammal species Shiu, Yu Palmer, K. J. Roch, Marie A. Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Klinck, Holger 2020-01-17 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969184/ http://www.ncbi.nlm.nih.gov/pubmed/31953462 https://doi.org/10.1038/s41598-020-57549-y en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969184/ http://www.ncbi.nlm.nih.gov/pubmed/31953462 http://dx.doi.org/10.1038/s41598-020-57549-y © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. CC-BY Article Text 2020 ftpubmed https://doi.org/10.1038/s41598-020-57549-y 2020-01-26T01:30:34Z Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species. Text Eubalaena glacialis North Atlantic PubMed Central (PMC) Scientific Reports 10 1 |
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Article Shiu, Yu Palmer, K. J. Roch, Marie A. Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Klinck, Holger Deep neural networks for automated detection of marine mammal species |
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Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species. |
format |
Text |
author |
Shiu, Yu Palmer, K. J. Roch, Marie A. Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Klinck, Holger |
author_facet |
Shiu, Yu Palmer, K. J. Roch, Marie A. Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Klinck, Holger |
author_sort |
Shiu, Yu |
title |
Deep neural networks for automated detection of marine mammal species |
title_short |
Deep neural networks for automated detection of marine mammal species |
title_full |
Deep neural networks for automated detection of marine mammal species |
title_fullStr |
Deep neural networks for automated detection of marine mammal species |
title_full_unstemmed |
Deep neural networks for automated detection of marine mammal species |
title_sort |
deep neural networks for automated detection of marine mammal species |
publisher |
Nature Publishing Group UK |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969184/ http://www.ncbi.nlm.nih.gov/pubmed/31953462 https://doi.org/10.1038/s41598-020-57549-y |
genre |
Eubalaena glacialis North Atlantic |
genre_facet |
Eubalaena glacialis North Atlantic |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969184/ http://www.ncbi.nlm.nih.gov/pubmed/31953462 http://dx.doi.org/10.1038/s41598-020-57549-y |
op_rights |
© The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
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CC-BY |
op_doi |
https://doi.org/10.1038/s41598-020-57549-y |
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