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To address this, an open-source framework for deep learning in bioacoustics to automatically detect Bornean white-bearded gibbon (Hylobates albibarbis) \u201cgreat call\u201d 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\u2009=\u20090.87), identifying 98% of the highest-quality calls from 90\u2009h 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.<\/jats:p>","DOI":"10.1121\/10.0028268","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T08:13:21Z","timestamp":1725869601000},"page":"1623-1632","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":5,"title":["Automated detection of Bornean white-bearded gibbon (\n                    <i>Hylobates albibarbis<\/i>\n                    ) vocalizations using an open-source framework for deep learning"],"prefix":"10.1121","volume":"156","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8456-5606","authenticated-orcid":false,"given":"A. 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Lisa Yang Center for Conservation Bioacoustics, Cornell Laboratory of Ornithology, Cornell University 5 , Ithaca, New York 14850, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3182-8612","authenticated-orcid":false,"family":"Mariaty","sequence":"additional","affiliation":[{"name":"Fakultas Kehutanan dan Pertanian, Universitas Muhammadiyah Palangka Raya 6 , Palangka Raya, 73111, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2667-5199","authenticated-orcid":false,"given":"Tatang Mitra","family":"Setia","sequence":"additional","affiliation":[{"name":"Department of Biology, Faculty of Biology and Agriculture, Universitas Nasional 7 , Jakarta, 12520, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9957-3153","authenticated-orcid":false,"given":"Manmohan","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Science, Faculty of Environment, Science and Economy, University of Exeter 1 , Penryn, TR10 9FE, United Kingdom"}]},{"given":"Siti","family":"Maimunah","sequence":"additional","affiliation":[{"name":"Fakultas Kehutanan, Instiper Yogyakarta 8 , Yogyakarta, 55281, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0708-5492","authenticated-orcid":false,"given":"F. J. F.","family":"Van Veen","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Science, Faculty of Environment, Science and Economy, University of Exeter 1 , Penryn, TR10 9FE, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5714-9496","authenticated-orcid":false,"given":"Wendy M.","family":"Erb","sequence":"additional","affiliation":[{"name":"K. Lisa Yang Center for Conservation Bioacoustics, Cornell Laboratory of Ornithology, Cornell University 5 , Ithaca, New York 14850, USA"}]}],"member":"231","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"issue":"4","key":"2024090912102856100_c1","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.ecoinf.2009.06.005","article-title":"Automated classification of bird and amphibian calls using machine learning: A comparison of methods","volume":"4","year":"2009","journal-title":"Ecol. 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