ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation

• Published in: Scientific Reports
• Main Authors: Hauer, Christopher, Nöth, Elmar, Barnhill, Alexander, Maier, Andreas, Guthunz, Julius, Hofer, Heribert, Cheng, Rachael Xi, Barth, Volker, Bergler, Christian
• Language: English
• Published: 2023
• Subjects: Deep Learning [MeSH]; Software [MeSH]; Computer Simulation [MeSH]; Animals [MeSH]; Sound [MeSH]; Whale; Killer [MeSH]; Killer Whale; Orca; Orcinus orca; Killer whale
• Online Access: https://repository.publisso.de/resource/frl:6482780; https://doi.org/10.1038/s41598-023-38132-7; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333356/

Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01. ORCA-SPY was field-tested on Lake Stechlin in Brandenburg Germany under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19 and a median error of 17.54. ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern ...