Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ...
Whistle contour extraction aims to derive animal whistles from time-frequency spectrograms as polylines. For toothed whales, whistle extraction results can serve as the basis for analyzing animal abundance, species identity, and social activities. During the last few decades, as long-term recording...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2304.02714 https://arxiv.org/abs/2304.02714 |
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ftdatacite:10.48550/arxiv.2304.02714 2023-06-11T04:17:20+02:00 Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... Li, Pu Roch, Marie Klinck, Holger Fleishman, Erica Gillespie, Douglas Nosal, Eva-Marie Shiu, Yu Liu, Xiaobai 2023 https://dx.doi.org/10.48550/arxiv.2304.02714 https://arxiv.org/abs/2304.02714 unknown arXiv https://dx.doi.org/10.1109/tmm.2023.3251109 Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 Computer Vision and Pattern Recognition cs.CV Signal Processing eess.SP FOS Computer and information sciences FOS Electrical engineering, electronic engineering, information engineering article-journal Text Article ScholarlyArticle 2023 ftdatacite https://doi.org/10.48550/arxiv.2304.0271410.1109/tmm.2023.3251109 2023-05-02T09:45:24Z Whistle contour extraction aims to derive animal whistles from time-frequency spectrograms as polylines. For toothed whales, whistle extraction results can serve as the basis for analyzing animal abundance, species identity, and social activities. During the last few decades, as long-term recording systems have become affordable, automated whistle extraction algorithms were proposed to process large volumes of recording data. Recently, a deep learning-based method demonstrated superior performance in extracting whistles under varying noise conditions. However, training such networks requires a large amount of labor-intensive annotation, which is not available for many species. To overcome this limitation, we present a framework of stage-wise generative adversarial networks (GANs), which compile new whistle data suitable for deep model training via three stages: generation of background noise in the spectrogram, generation of whistle contours, and generation of whistle signals. By separating the generation of ... : Accepted by IEEE Transactions of Multimedia (2023) ... Text toothed whales DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Computer Vision and Pattern Recognition cs.CV Signal Processing eess.SP FOS Computer and information sciences FOS Electrical engineering, electronic engineering, information engineering |
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Computer Vision and Pattern Recognition cs.CV Signal Processing eess.SP FOS Computer and information sciences FOS Electrical engineering, electronic engineering, information engineering Li, Pu Roch, Marie Klinck, Holger Fleishman, Erica Gillespie, Douglas Nosal, Eva-Marie Shiu, Yu Liu, Xiaobai Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... |
topic_facet |
Computer Vision and Pattern Recognition cs.CV Signal Processing eess.SP FOS Computer and information sciences FOS Electrical engineering, electronic engineering, information engineering |
description |
Whistle contour extraction aims to derive animal whistles from time-frequency spectrograms as polylines. For toothed whales, whistle extraction results can serve as the basis for analyzing animal abundance, species identity, and social activities. During the last few decades, as long-term recording systems have become affordable, automated whistle extraction algorithms were proposed to process large volumes of recording data. Recently, a deep learning-based method demonstrated superior performance in extracting whistles under varying noise conditions. However, training such networks requires a large amount of labor-intensive annotation, which is not available for many species. To overcome this limitation, we present a framework of stage-wise generative adversarial networks (GANs), which compile new whistle data suitable for deep model training via three stages: generation of background noise in the spectrogram, generation of whistle contours, and generation of whistle signals. By separating the generation of ... : Accepted by IEEE Transactions of Multimedia (2023) ... |
format |
Text |
author |
Li, Pu Roch, Marie Klinck, Holger Fleishman, Erica Gillespie, Douglas Nosal, Eva-Marie Shiu, Yu Liu, Xiaobai |
author_facet |
Li, Pu Roch, Marie Klinck, Holger Fleishman, Erica Gillespie, Douglas Nosal, Eva-Marie Shiu, Yu Liu, Xiaobai |
author_sort |
Li, Pu |
title |
Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... |
title_short |
Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... |
title_full |
Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... |
title_fullStr |
Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... |
title_full_unstemmed |
Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ... |
title_sort |
learning stage-wise gans for whistle extraction in time-frequency spectrograms ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2304.02714 https://arxiv.org/abs/2304.02714 |
genre |
toothed whales |
genre_facet |
toothed whales |
op_relation |
https://dx.doi.org/10.1109/tmm.2023.3251109 |
op_rights |
Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2304.0271410.1109/tmm.2023.3251109 |
_version_ |
1768376429716701184 |