ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://refubium.fu-berlin.de/handle/fub188/40768 https://doi.org/10.17169/refubium-40489 https://doi.org/10.1038/s41598-023-38132-7 |
| Summary: | 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 ... |
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