Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ...
The acoustic data were collected between 2015 and 2019 at six stations in the southern Gulf of St. Lawrence using two different deployment configurations. The dataset also includes audio samples from the Gulf of Maine recorded in 2009, which were part of the DCLDE 2013 Challenge dataset (https://soi...
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Federated Research Data Repository / dépôt fédéré de données de recherche
2020
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Online Access: | https://dx.doi.org/10.20383/101.0241 https://www.frdr-dfdr.ca/repo/dataset/4a3113e6-1d58-6bb4-aaf2-a9adf75165be |
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ftdatacite:10.20383/101.0241 2024-02-27T08:40:16+00:00 Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... Simard, Yvan Kirsebom, Oliver S. Frazao, Fabio Roy, Nathalie Matwin, Stan Giard, Samuel 2020 https://dx.doi.org/10.20383/101.0241 https://www.frdr-dfdr.ca/repo/dataset/4a3113e6-1d58-6bb4-aaf2-a9adf75165be unknown Federated Research Data Repository / dépôt fédéré de données de recherche Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 North Atlantic right whale Passive Acoustic Monitoring Underwater Acoustics Signal Processing Deep Neural Networks Eubalaena glacialis Animal behaviour Comportement animal dataset Dataset 2020 ftdatacite https://doi.org/10.20383/101.0241 2024-02-01T14:53:49Z The acoustic data were collected between 2015 and 2019 at six stations in the southern Gulf of St. Lawrence using two different deployment configurations. The dataset also includes audio samples from the Gulf of Maine recorded in 2009, which were part of the DCLDE 2013 Challenge dataset (https://soi.st-andrews.ac.uk/static/soi/dclde2013/documents/WorkshopDataset2013.pdf). The acoustic data are supplemented by expert annotations indicating the presence/absence of North Atlantic right whale upcalls. Spectrogram images are also provided. These data have been used to train a deep neural network to detect North Atlantic right whale upcalls, as described in the article "Performance of a Deep Neural Network at Detecting North Atlantic Right Whale Upcalls" by Kirsebom, Frazao, Simard, Roy, Matwin, and Giard (doi:10.1121/10.0001132), which also contains a detailed description of the data. The software developed as part of this work is available on Zenodo (doi:10.5281/zenodo.3736625) under a GPLv3 license. This ... Dataset Eubalaena glacialis North Atlantic North Atlantic right whale DataCite Metadata Store (German National Library of Science and Technology) Giard ENVELOPE(-63.806,-63.806,-64.428,-64.428) Soi ENVELOPE(30.704,30.704,66.481,66.481) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
North Atlantic right whale Passive Acoustic Monitoring Underwater Acoustics Signal Processing Deep Neural Networks Eubalaena glacialis Animal behaviour Comportement animal |
spellingShingle |
North Atlantic right whale Passive Acoustic Monitoring Underwater Acoustics Signal Processing Deep Neural Networks Eubalaena glacialis Animal behaviour Comportement animal Simard, Yvan Kirsebom, Oliver S. Frazao, Fabio Roy, Nathalie Matwin, Stan Giard, Samuel Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... |
topic_facet |
North Atlantic right whale Passive Acoustic Monitoring Underwater Acoustics Signal Processing Deep Neural Networks Eubalaena glacialis Animal behaviour Comportement animal |
description |
The acoustic data were collected between 2015 and 2019 at six stations in the southern Gulf of St. Lawrence using two different deployment configurations. The dataset also includes audio samples from the Gulf of Maine recorded in 2009, which were part of the DCLDE 2013 Challenge dataset (https://soi.st-andrews.ac.uk/static/soi/dclde2013/documents/WorkshopDataset2013.pdf). The acoustic data are supplemented by expert annotations indicating the presence/absence of North Atlantic right whale upcalls. Spectrogram images are also provided. These data have been used to train a deep neural network to detect North Atlantic right whale upcalls, as described in the article "Performance of a Deep Neural Network at Detecting North Atlantic Right Whale Upcalls" by Kirsebom, Frazao, Simard, Roy, Matwin, and Giard (doi:10.1121/10.0001132), which also contains a detailed description of the data. The software developed as part of this work is available on Zenodo (doi:10.5281/zenodo.3736625) under a GPLv3 license. This ... |
format |
Dataset |
author |
Simard, Yvan Kirsebom, Oliver S. Frazao, Fabio Roy, Nathalie Matwin, Stan Giard, Samuel |
author_facet |
Simard, Yvan Kirsebom, Oliver S. Frazao, Fabio Roy, Nathalie Matwin, Stan Giard, Samuel |
author_sort |
Simard, Yvan |
title |
Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... |
title_short |
Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... |
title_full |
Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... |
title_fullStr |
Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... |
title_full_unstemmed |
Acoustic recordings of North Atlantic right whale upcalls in the Gulf of St. Lawrence ... |
title_sort |
acoustic recordings of north atlantic right whale upcalls in the gulf of st. lawrence ... |
publisher |
Federated Research Data Repository / dépôt fédéré de données de recherche |
publishDate |
2020 |
url |
https://dx.doi.org/10.20383/101.0241 https://www.frdr-dfdr.ca/repo/dataset/4a3113e6-1d58-6bb4-aaf2-a9adf75165be |
long_lat |
ENVELOPE(-63.806,-63.806,-64.428,-64.428) ENVELOPE(30.704,30.704,66.481,66.481) |
geographic |
Giard Soi |
geographic_facet |
Giard Soi |
genre |
Eubalaena glacialis North Atlantic North Atlantic right whale |
genre_facet |
Eubalaena glacialis North Atlantic North Atlantic right whale |
op_rights |
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.20383/101.0241 |
_version_ |
1792047378380881920 |