Publikationsdatenbank

ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation (2023)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Hauer, Christopher

Nöth, Elmar

Barnhill, Alexander

Maier, Andreas

Guthunz, Julius

Hofer, Heribert (WE 13)

Cheng, Rachael Xi

Barth, Volker

Bergler, Christian

Quelle

Scientific reports

Bandzählung: 13

Heftzählung: 1

Seiten: Artikel 11106

ISSN: 2045-2322

Sprache

Englisch

Verweise

URL (Volltext): https://www.nature.com/articles/s41598-023-38132-7

DOI: 10.1038/s41598-023-38132-7

Pubmed: 37429871

Kontakt

Institut für Parasitologie und Tropenveterinärmedizin

Robert-von-Ostertag-Str. 7-13
14163 Berlin
+49 30 838 62310
parasitologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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 [Formula: see text] dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01[Formula: see text]. 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[Formula: see text] and a median error of 17.54[Formula: see text]. ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01[Formula: see text] and a median error of 11.01[Formula: see text] 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.