Improve automatic detection of animal call sequences with temporal context
Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences...
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Online Access: | http://dx.doi.org/10.1098/rsif.2021.0297 https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0297 https://royalsocietypublishing.org/doi/full-xml/10.1098/rsif.2021.0297 |
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crroyalsociety:10.1098/rsif.2021.0297 2024-06-23T07:51:33+00:00 Improve automatic detection of animal call sequences with temporal context Madhusudhana, Shyam Shiu, Yu Klinck, Holger Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Širović, Ana Roch, Marie A. Office of Naval Research 2021 http://dx.doi.org/10.1098/rsif.2021.0297 https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0297 https://royalsocietypublishing.org/doi/full-xml/10.1098/rsif.2021.0297 en eng The Royal Society https://royalsociety.org/journals/ethics-policies/data-sharing-mining/ Journal of The Royal Society Interface volume 18, issue 180, page 20210297 ISSN 1742-5662 journal-article 2021 crroyalsociety https://doi.org/10.1098/rsif.2021.0297 2024-06-10T04:15:16Z Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale ( Balaenoptera physalus ) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9–17% increase in area under the precision–recall curve and a 9–18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings. Article in Journal/Newspaper Balaenoptera physalus Fin whale The Royal Society Journal of The Royal Society Interface 18 180 20210297 |
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The Royal Society |
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crroyalsociety |
language |
English |
description |
Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale ( Balaenoptera physalus ) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9–17% increase in area under the precision–recall curve and a 9–18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings. |
author2 |
Office of Naval Research |
format |
Article in Journal/Newspaper |
author |
Madhusudhana, Shyam Shiu, Yu Klinck, Holger Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Širović, Ana Roch, Marie A. |
spellingShingle |
Madhusudhana, Shyam Shiu, Yu Klinck, Holger Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Širović, Ana Roch, Marie A. Improve automatic detection of animal call sequences with temporal context |
author_facet |
Madhusudhana, Shyam Shiu, Yu Klinck, Holger Fleishman, Erica Liu, Xiaobai Nosal, Eva-Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Širović, Ana Roch, Marie A. |
author_sort |
Madhusudhana, Shyam |
title |
Improve automatic detection of animal call sequences with temporal context |
title_short |
Improve automatic detection of animal call sequences with temporal context |
title_full |
Improve automatic detection of animal call sequences with temporal context |
title_fullStr |
Improve automatic detection of animal call sequences with temporal context |
title_full_unstemmed |
Improve automatic detection of animal call sequences with temporal context |
title_sort |
improve automatic detection of animal call sequences with temporal context |
publisher |
The Royal Society |
publishDate |
2021 |
url |
http://dx.doi.org/10.1098/rsif.2021.0297 https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0297 https://royalsocietypublishing.org/doi/full-xml/10.1098/rsif.2021.0297 |
genre |
Balaenoptera physalus Fin whale |
genre_facet |
Balaenoptera physalus Fin whale |
op_source |
Journal of The Royal Society Interface volume 18, issue 180, page 20210297 ISSN 1742-5662 |
op_rights |
https://royalsociety.org/journals/ethics-policies/data-sharing-mining/ |
op_doi |
https://doi.org/10.1098/rsif.2021.0297 |
container_title |
Journal of The Royal Society Interface |
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18 |
container_issue |
180 |
container_start_page |
20210297 |
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1802642666210459648 |