Deep neural networks for automated detection of marine mammal species

• Published in: Scientific Reports
• Main Authors: Shiu, Yu, Palmer, Kaitlin, Roch, Marie, Fleishman, Erica, Liu, Xiaobai, Nosal, Eva-Marie, Helble, Tyler, Cholewiak, Danielle, Gillespie, Douglas Michael, Klinck, Holger
• Other Authors: University of St Andrews. School of Biology, University of St Andrews. Sea Mammal Research Unit, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Sound Tags Group, University of St Andrews. Bioacoustics group, University of St Andrews. Marine Alliance for Science & Technology Scotland
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2020
• Subjects: QH301 Biology; 3rd-DAS; SDG 14 - Life Below Water; QH301; Eubalaena glacialis; North Atlantic
• Online Access: http://hdl.handle.net/10023/19305; https://doi.org/10.1038/s41598-020-57549-y

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867). Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species. Publisher PDF Peer reviewed