ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most...
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ftleibnizopen:oai:oai.leibnizopen.de:sZJN04kBdbrxVwz6mnfx 2023-10-01T03:57:09+02:00 ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning Bergler, Michael Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas 2019 https://repository.publisso.de/resource/frl:6419471 https://doi.org/10.1038/s41598-019-47335-w https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662697/ eng eng http://creativecommons.org/licenses/by/4.0/ Scientific reports, 9:10997 Animal behaviour Behavioural ecology 2019 ftleibnizopen https://doi.org/10.1038/s41598-019-47335-w 2023-09-03T23:22:32Z Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species. Other/Unknown Material Killer Whale Orca Orcinus orca Killer whale LeibnizOpen (The Leibniz Association) Scientific Reports 9 1 |
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Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
Animal behaviour Behavioural ecology |
spellingShingle |
Animal behaviour Behavioural ecology Bergler, Michael Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning |
topic_facet |
Animal behaviour Behavioural ecology |
description |
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species. |
author |
Bergler, Michael Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas |
author_facet |
Bergler, Michael Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas |
author_sort |
Bergler, Michael |
title |
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning |
title_short |
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning |
title_full |
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning |
title_fullStr |
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning |
title_full_unstemmed |
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning |
title_sort |
orca-spot: an automatic killer whale sound detection toolkit using deep learning |
publishDate |
2019 |
url |
https://repository.publisso.de/resource/frl:6419471 https://doi.org/10.1038/s41598-019-47335-w https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662697/ |
genre |
Killer Whale Orca Orcinus orca Killer whale |
genre_facet |
Killer Whale Orca Orcinus orca Killer whale |
op_source |
Scientific reports, 9:10997 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1038/s41598-019-47335-w |
container_title |
Scientific Reports |
container_volume |
9 |
container_issue |
1 |
_version_ |
1778528179795787776 |