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...
Main Authors: | , , , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://refubium.fu-berlin.de/handle/fub188/25315 https://doi.org/10.17169/refubium-4018 https://doi.org/10.1038/s41598-019-47335-w |
id |
ftfuberlin:oai:refubium.fu-berlin.de:fub188/25315 |
---|---|
record_format |
openpolar |
spelling |
ftfuberlin:oai:refubium.fu-berlin.de:fub188/25315 2023-05-15T17:03:23+02:00 ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning Bergler, Christian Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas 2019 17 Seiten application/pdf https://refubium.fu-berlin.de/handle/fub188/25315 https://doi.org/10.17169/refubium-4018 https://doi.org/10.1038/s41598-019-47335-w eng eng https://refubium.fu-berlin.de/handle/fub188/25315 http://dx.doi.org/10.17169/refubium-4018 doi:10.1038/s41598-019-47335-w https://creativecommons.org/licenses/by/4.0/ CC-BY animal behaviour behavioural ecology ddc:599 doc-type:article 2019 ftfuberlin https://doi.org/10.17169/refubium-4018 https://doi.org/10.1038/s41598-019-47335-w 2022-05-15T20:47:54Z 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. Article in Journal/Newspaper Killer Whale Orca Orcinus orca Killer whale Freie Universität Berlin: Refubium (FU Berlin) |
institution |
Open Polar |
collection |
Freie Universität Berlin: Refubium (FU Berlin) |
op_collection_id |
ftfuberlin |
language |
English |
topic |
animal behaviour behavioural ecology ddc:599 |
spellingShingle |
animal behaviour behavioural ecology ddc:599 Bergler, Christian 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 ddc:599 |
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. |
format |
Article in Journal/Newspaper |
author |
Bergler, Christian Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas |
author_facet |
Bergler, Christian Schröter, Hendrik Cheng, Rachael Xi Barth, Volker Weber, Michael Nöth, Elmar Hofer, Heribert Maier, Andreas |
author_sort |
Bergler, Christian |
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://refubium.fu-berlin.de/handle/fub188/25315 https://doi.org/10.17169/refubium-4018 https://doi.org/10.1038/s41598-019-47335-w |
genre |
Killer Whale Orca Orcinus orca Killer whale |
genre_facet |
Killer Whale Orca Orcinus orca Killer whale |
op_relation |
https://refubium.fu-berlin.de/handle/fub188/25315 http://dx.doi.org/10.17169/refubium-4018 doi:10.1038/s41598-019-47335-w |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.17169/refubium-4018 https://doi.org/10.1038/s41598-019-47335-w |
_version_ |
1766057245000335360 |