Killer whales (Orcinus orca) exhibit significant ecological and genetic diversity, with three primary sympatric ecotypes in the Northeast Pacific: Resident, Bigg’s (Transient), and Offshore. Each ecotype is adapted to distinct ecological niches, with unique foraging strategies, social structures, and vocal behaviors. These differences underscore the challenges in monitoring and conserving each group, as they coexist within overlapping geographic ranges yet do not intermix. This dataset, compiled from diverse sources, provides a comprehensive resource for the detection and classification of killer whale vocalizations. The >225,000 call-level annotations collected from 23 locations, and a cleaned annotation csv that collates annotations across the different protocols. Recordings spanning eleven years from various geographical locations within the Northeast Pacific Ocean, collected using multiple hydrophone systems. It addresses the challenge of differentiating killer whale calls from other marine species and environmental noise and includes specific instances of confounding signals to enhance model robustness. Detailed annotations capture a broad spectrum of vocalizations and 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 ecotypes.
About this Dataset
Title | DCLDE 2026: Killer whale (Orcinus orca) ecotype and other species annotations for the Detection Classification Localization and Density Estimate (DCLDE) conference in 2026 |
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Description | Killer whales (Orcinus orca) exhibit significant ecological and genetic diversity, with three primary sympatric ecotypes in the Northeast Pacific: Resident, Bigg’s (Transient), and Offshore. Each ecotype is adapted to distinct ecological niches, with unique foraging strategies, social structures, and vocal behaviors. These differences underscore the challenges in monitoring and conserving each group, as they coexist within overlapping geographic ranges yet do not intermix. This dataset, compiled from diverse sources, provides a comprehensive resource for the detection and classification of killer whale vocalizations. The >225,000 call-level annotations collected from 23 locations, and a cleaned annotation csv that collates annotations across the different protocols. Recordings spanning eleven years from various geographical locations within the Northeast Pacific Ocean, collected using multiple hydrophone systems. It addresses the challenge of differentiating killer whale calls from other marine species and environmental noise and includes specific instances of confounding signals to enhance model robustness. Detailed annotations capture a broad spectrum of vocalizations and 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 ecotypes. |
Modified | 2025-04-20T10:17:54.102Z |
Publisher Name | N/A |
Contact | N/A |
Keywords | Earth Science > Biological Classification > Animals/Vertebrates > Mammals > Cetaceans , Earth Science > Oceans > Ocean Acoustics , Earth Science > Oceans > Aquatic Sciences > Fisheries , Earth Science > Oceans > Marine Environment Monitoring , Earth Science > Biosphere > Aquatic Ecosystems , Earth Science > Biosphere > Ecosystems > Marine Ecosystems , Ocean > Pacific Ocean > Eastern Pacific Ocean , Fixed Observation Stations , Recorders/Loggers > Passive Acoustic Recorder , Acoustic Sounders > Hydrophones , DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce , biota , oceans |
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