![]() This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All training and test data, including user and expert annotations, along with the code to train and evaluate our detection algorithms are available on our GitHub page ( ).įunding: This work was supported financially through the Darwin Initiative (Awards 15003, 161333, EIDPR075), the Zooniverse, the People’s Trust for Endangered Species, Mammals Trust UK, the Leverhulme Trust (Philip Leverhulme Prize for KEJ), NERC (NE/P016677/1), and EPSRC (EP/K015664/1 and EP/K503745/1). Received: AugAccepted: JanuPublished: March 8, 2018Ĭopyright: © 2018 Mac Aodha et al. PLoS Comput Biol 14(3):Įditor: Brock Fenton, University of Western Ontario, CANADA (2018) Bat detective-Deep learning tools for bat acoustic signal detection. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.Ĭitation: Mac Aodha O, Gibb R, Barlow KE, Browning E, Firman M, Freeman R, et al. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species.
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