A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification
Abstract Killer whales (Orcinus orca) exhibit significant ecological and genetic diversity, with three primary sympatric populations in the Northeast Pacific: Resident, Bigg’s (Transient), and Offshore. Each population is characterized by distinct foraging habits, social structures, and vocal repert...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05281-5 |
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| author | K. J. Palmer Emma Cummings Michael G. Dowd Kait Frasier Fabio Frazao Alex Harris April Houweling Jasper Kanes Oliver S. Kirsebom Holger Klinck Holly LeBlond Lauren Laturnus Craig Matkin Olivia Murphy Hannah Myers Dan Olsen Caitlin O’Neill Bruno Padovese James Pilkington Lucy Quayle Amalis Riera Vuibert Krista Trounce Svein Vagle Scott Veirs Val Veirs Jen Wladichuk Jason Wood Tina Yack Harald Yurk Ruth Joy |
| author_facet | K. J. Palmer Emma Cummings Michael G. Dowd Kait Frasier Fabio Frazao Alex Harris April Houweling Jasper Kanes Oliver S. Kirsebom Holger Klinck Holly LeBlond Lauren Laturnus Craig Matkin Olivia Murphy Hannah Myers Dan Olsen Caitlin O’Neill Bruno Padovese James Pilkington Lucy Quayle Amalis Riera Vuibert Krista Trounce Svein Vagle Scott Veirs Val Veirs Jen Wladichuk Jason Wood Tina Yack Harald Yurk Ruth Joy |
| author_sort | K. J. Palmer |
| collection | DOAJ |
| description | Abstract Killer whales (Orcinus orca) exhibit significant ecological and genetic diversity, with three primary sympatric populations in the Northeast Pacific: Resident, Bigg’s (Transient), and Offshore. Each population is characterized by distinct foraging habits, social structures, and vocal repertoires, which complicate accurate monitoring and conservation efforts. This dataset, compiled from diverse sources, provides a comprehensive resource for the detection and classification of killer whale vocalizations. The dataset includes annotated acoustic recordings spanning 11 years from various locations in Alaska, British Columbia, and Washington, collected using multiple hydrophone systems. It addresses the challenge of differentiating killer whale calls from other marine species and environmental noise, including specific instances of confounding signals that may help enhance model robustness. Detailed annotations capture a diverse suite of vocalizations and their associated metadata, facilitating the development of advanced machine learning models for ecological monitoring. This curated dataset aims to improve the accuracy of killer whale detection algorithms, support conservation efforts, and advance our understanding of killer whale acoustic communication across different populations. |
| format | Article |
| id | doaj-art-82344ff09fb34b088bfde1ba2989b64b |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-82344ff09fb34b088bfde1ba2989b64b2025-08-20T03:45:18ZengNature PortfolioScientific Data2052-44632025-07-0112111510.1038/s41597-025-05281-5A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype ClassificationK. J. Palmer0Emma Cummings1Michael G. Dowd2Kait Frasier3Fabio Frazao4Alex Harris5April Houweling6Jasper Kanes7Oliver S. Kirsebom8Holger Klinck9Holly LeBlond10Lauren Laturnus11Craig Matkin12Olivia Murphy13Hannah Myers14Dan Olsen15Caitlin O’Neill16Bruno Padovese17James Pilkington18Lucy Quayle19Amalis Riera Vuibert20Krista Trounce21Svein Vagle22Scott Veirs23Val Veirs24Jen Wladichuk25Jason Wood26Tina Yack27Harald Yurk28Ruth Joy29Department of Environmental Science, Simon Fraser UniversityDepartment of Environmental Science, Simon Fraser UniversityDept of Mathematics & Statistics, Dalhousie UniversityScripps Institution of OceanographyFaculty of Computer Science, Dalhousie UniversityDepartment of Environmental Science, Simon Fraser UniversityDepartment of Statistics and Actuarial Science, Simon Fraser UniversityOcean Networks Canada, University of VictoriaFaculty of Computer Science, Dalhousie UniversityLisa K Yang Center for Conservation Bioacoustics, Cornell UniversityInstitute of Ocean Sciences, Fisheries and Oceans CanadaDepartment of Environmental Science, Simon Fraser UniversityNorth Gulf Oceanic SocietyDepartment of Environmental Science, Simon Fraser UniversityMarine Mammal Institute, Oregon State UniversityNorth Gulf Oceanic SocietyPacific Science Enterprise Centre, Fisheries and Oceans CanadaDepartment of Environmental Science, Simon Fraser UniversityPacific Biological Station, Fisheries and Oceans CanadaPacific Science Enterprise Centre, Fisheries and Oceans CanadaDepartment of Biology, University of VictoriaVancouver Fraser Port AuthorityPacific Science Enterprise Centre, Fisheries and Oceans CanadaBeam ReachBeam ReachJASCO Applied Sciences Ltd.SMRU ConsultingSMRU ConsultingDepartment of Environmental Science, Simon Fraser UniversityDepartment of Environmental Science, Simon Fraser UniversityAbstract Killer whales (Orcinus orca) exhibit significant ecological and genetic diversity, with three primary sympatric populations in the Northeast Pacific: Resident, Bigg’s (Transient), and Offshore. Each population is characterized by distinct foraging habits, social structures, and vocal repertoires, which complicate accurate monitoring and conservation efforts. This dataset, compiled from diverse sources, provides a comprehensive resource for the detection and classification of killer whale vocalizations. The dataset includes annotated acoustic recordings spanning 11 years from various locations in Alaska, British Columbia, and Washington, collected using multiple hydrophone systems. It addresses the challenge of differentiating killer whale calls from other marine species and environmental noise, including specific instances of confounding signals that may help enhance model robustness. Detailed annotations capture a diverse suite of vocalizations and their associated metadata, facilitating the development of advanced machine learning models for ecological monitoring. This curated dataset aims to improve the accuracy of killer whale detection algorithms, support conservation efforts, and advance our understanding of killer whale acoustic communication across different populations.https://doi.org/10.1038/s41597-025-05281-5 |
| spellingShingle | K. J. Palmer Emma Cummings Michael G. Dowd Kait Frasier Fabio Frazao Alex Harris April Houweling Jasper Kanes Oliver S. Kirsebom Holger Klinck Holly LeBlond Lauren Laturnus Craig Matkin Olivia Murphy Hannah Myers Dan Olsen Caitlin O’Neill Bruno Padovese James Pilkington Lucy Quayle Amalis Riera Vuibert Krista Trounce Svein Vagle Scott Veirs Val Veirs Jen Wladichuk Jason Wood Tina Yack Harald Yurk Ruth Joy A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification Scientific Data |
| title | A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification |
| title_full | A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification |
| title_fullStr | A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification |
| title_full_unstemmed | A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification |
| title_short | A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification |
| title_sort | public dataset of annotated orcinus orca acoustic signals for detection and ecotype classification |
| url | https://doi.org/10.1038/s41597-025-05281-5 |
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