Deep neural networks for automated detection of marine mammal species

TitleDeep neural networks for automated detection of marine mammal species
Publication TypeJournal Article
Year of Publication2020
AuthorsShiu, Yu, Palmer K. J., Roch Marie A., Fleishman Erica, Liu Xiaobai, Nosal Eva-Marie, Helble Tyler, Cholewiak Danielle, Gillespie Douglas, and Klinck Holger
JournalScientific Reports
Volume10
Pagination607
ISBN Number2045-2322
EndNote Rec Number12977
Abstract

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.