Automated detection of Bornean white-bearded gibbon (Hylobates albibarbis) vocalizations using an open-source framework for deep learning.

Autor:	Owens AF; Department of Earth and Environmental Science, Faculty of Environment, Science and Economy, University of Exeter, Penryn, TR10 9FE, United Kingdom., Hockings KJ; Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, Penryn, TR10 9FE, United Kingdom., Imron MA; Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia., Madhusudhana S; Centre for Marine Science and Technology, Curtin University, Perth, Western Australia, 6102, Australia.; K. Lisa Yang Center for Conservation Bioacoustics, Cornell Laboratory of Ornithology, Cornell University, Ithaca, New York 14850, USA., Mariaty; Fakultas Kehutanan dan Pertanian, Universitas Muhammadiyah Palangka Raya, Palangka Raya, 73111, Indonesia., Setia TM; Department of Biology, Faculty of Biology and Agriculture, Universitas Nasional, Jakarta, 12520, Indonesia., Sharma M; Department of Earth and Environmental Science, Faculty of Environment, Science and Economy, University of Exeter, Penryn, TR10 9FE, United Kingdom., Maimunah S; Fakultas Kehutanan, Instiper Yogyakarta, Yogyakarta, 55281, Indonesia., Van Veen FJF; Department of Earth and Environmental Science, Faculty of Environment, Science and Economy, University of Exeter, Penryn, TR10 9FE, United Kingdom., Erb WM; K. Lisa Yang Center for Conservation Bioacoustics, Cornell Laboratory of Ornithology, Cornell University, Ithaca, New York 14850, USA.
Jazyk:	angličtina
Zdroj:	The Journal of the Acoustical Society of America [J Acoust Soc Am] 2024 Sep 01; Vol. 156 (3), pp. 1623-1632.
DOI:	10.1121/10.0028268
Abstrakt:	Passive acoustic monitoring is a promising tool for monitoring at-risk populations of vocal species, yet, extracting relevant information from large acoustic datasets can be time-consuming, creating a bottleneck at the point of analysis. To address this, an open-source framework for deep learning in bioacoustics to automatically detect Bornean white-bearded gibbon (Hylobates albibarbis) "great call" vocalizations in a long-term acoustic dataset from a rainforest location in Borneo is adapted. The steps involved in developing this solution are described, including collecting audio recordings, developing training and testing datasets, training neural network models, and evaluating model performance. The best model performed at a satisfactory level (F score = 0.87), identifying 98% of the highest-quality calls from 90 h of manually annotated audio recordings and greatly reduced analysis times when compared to a human observer. No significant difference was found in the temporal distribution of great call detections between the manual annotations and the model's output. Future work should seek to apply this model to long-term acoustic datasets to understand spatiotemporal variations in H. albibarbis' calling activity. Overall, a roadmap is presented for applying deep learning to identify the vocalizations of species of interest, which can be adapted for monitoring other endangered vocalizing species. (© 2024 Acoustical Society of America.)
Databáze:	MEDLINE
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