Deep neural networks for automated detection of marine mammal species
Abstract 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...
Published in: | Scientific Reports |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Springer Science and Business Media LLC
2020
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1038/s41598-020-57549-y https://www.nature.com/articles/s41598-020-57549-y.pdf https://www.nature.com/articles/s41598-020-57549-y |
id |
crspringernat:10.1038/s41598-020-57549-y |
---|---|
record_format |
openpolar |
spelling |
crspringernat:10.1038/s41598-020-57549-y 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 http://dx.doi.org/10.1038/s41598-020-57549-y https://www.nature.com/articles/s41598-020-57549-y.pdf https://www.nature.com/articles/s41598-020-57549-y en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Reports volume 10, issue 1 ISSN 2045-2322 Multidisciplinary journal-article 2020 crspringernat https://doi.org/10.1038/s41598-020-57549-y 2022-01-04T14:09:10Z Abstract 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. Article in Journal/Newspaper Eubalaena glacialis North Atlantic Springer Nature (via Crossref) Scientific Reports 10 1 |
institution |
Open Polar |
collection |
Springer Nature (via Crossref) |
op_collection_id |
crspringernat |
language |
English |
topic |
Multidisciplinary |
spellingShingle |
Multidisciplinary 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 |
topic_facet |
Multidisciplinary |
description |
Abstract 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 |
Article in Journal/Newspaper |
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 |
Springer Science and Business Media LLC |
publishDate |
2020 |
url |
http://dx.doi.org/10.1038/s41598-020-57549-y https://www.nature.com/articles/s41598-020-57549-y.pdf https://www.nature.com/articles/s41598-020-57549-y |
genre |
Eubalaena glacialis North Atlantic |
genre_facet |
Eubalaena glacialis North Atlantic |
op_source |
Scientific Reports volume 10, issue 1 ISSN 2045-2322 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-020-57549-y |
container_title |
Scientific Reports |
container_volume |
10 |
container_issue |
1 |
_version_ |
1766404361029681152 |