A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification

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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Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-05281-5
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Summary: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.
ISSN:2052-4463

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