ORCA-SPY: Killer Whale Sound Source Simulation and Detection, Classification and Localization in PAMGuard Utilizing Integrated Deep Learning Based Segmentation

Hauer C, Nöth E, Barnhill A, Maier A, Guthunz J, Hofer H, Cheng RX, Barth V, Bergler C (2023)

Publication Language: English

Publication Type: Journal article

Publication year: 2023

Journal

Book Volume: UNDER REVIEW

Abstract

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 (TDOA) 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 -14.2dB to 3dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01°. ORCA-SPY was field-tested on the lake Stechlin 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 at 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.

Authors with CRIS profile

Involved external institutions

Anthro-Media Documentary and iTV Production Germany (DE) Leibniz-Institut für Zoo- und Wildtierforschung (IZW) Germany (DE)

How to cite

APA:

Hauer, C., Nöth, E., Barnhill, A., Maier, A., Guthunz, J., Hofer, H.,... Bergler, C. (2023). ORCA-SPY: Killer Whale Sound Source Simulation and Detection, Classification and Localization in PAMGuard Utilizing Integrated Deep Learning Based Segmentation. Scientific Reports, UNDER REVIEW.

MLA:

Hauer, Christopher, et al. "ORCA-SPY: Killer Whale Sound Source Simulation and Detection, Classification and Localization in PAMGuard Utilizing Integrated Deep Learning Based Segmentation." Scientific Reports UNDER REVIEW (2023).

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