Palmer K J, Cummings Emma, Dowd Michael G, Frasier Kait, Frazao Fabio, Harris Alex, Houweling April, Kanes Jasper, Kirsebom Oliver S, Klinck Holger, LeBlond Holly, Laturnus Lauren, Matkin Craig, Murphy Olivia, Myers Hannah, Olsen Dan, O'Neill Caitlin, Padovese Bruno, Pilkington James, Quayle Lucy, Vuibert Amalis Riera, Trounce Krista, Vagle Svein, Veirs Scott, Veirs Val, Wladichuk Jen, Wood Jason, Yack Tina, Yurk Harald, Joy Ruth
Department of Environmental Science, Simon Fraser University, Burnaby, BC, Canada.
Dept of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada.
Sci Data. 2025 Jul 3;12(1):1137. doi: 10.1038/s41597-025-05281-5.
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.
虎鲸（逆戟鲸）表现出显著的生态和遗传多样性，在东北太平洋有三个主要的同域种群：定居型、比格氏（瞬态型）和远洋型。每个种群都有独特的觅食习性、社会结构和声音 repertoire，这使得准确监测和保护工作变得复杂。这个从不同来源汇编的数据集为虎鲸发声的检测和分类提供了一个全面的资源。该数据集包括来自阿拉斯加、不列颠哥伦比亚省和华盛顿州不同地点的 11 年带注释的声学记录，使用多个水听器系统收集。它解决了将虎鲸叫声与其他海洋物种和环境噪声区分开来的挑战，包括可能有助于提高模型鲁棒性的混淆信号的特定实例。详细的注释捕捉了各种各样的发声及其相关元数据，便于开发用于生态监测的先进机器学习模型。这个经过整理的数据集旨在提高虎鲸检测算法的准确性，支持保护工作，并增进我们对不同种群虎鲸声学交流的理解。
