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

• Published in: Scientific reports 2023-07, Vol.13 (1), p.11106-11106, Article 11106
• Main Authors: Hauer, Christopher, Nöth, Elmar, Barnhill, Alexander, Maier, Andreas, Guthunz, Julius, Hofer, Heribert, Cheng, Rachael Xi, Barth, Volker, Bergler, Christian
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/794; 704/829/826; Acoustic tracking; Animal species; Animals; Aquatic mammals; Classification; Computer Simulation; Deep Learning; Dolphins; Field study; Fieldwork; Humanities and Social Sciences; Hydrophones; Localization; Marine mammals; multidisciplinary; Orcinus orca; Science; Science (multidisciplinary); Software; Sound; Underwater; Vocalization behavior; Whale, Killer
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-38132-7

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 - 14.2  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 British Columbia, with a mean average error of 20.01 ∘ and a median error of 11.01 ∘ across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.