Deep neural networks for automated detection of marine mammal species Scientific Reports

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. 2020 31953462 PMC6969184 NMFS (National Marine Fisheries Service) NEFSC (Northeast Fisheries Science Center) PMC https://dx.doi.org/10.1038/s41598-020-57549-y CC BY 1955

深度神经网络（deep neural network）推动了检测与分类领域的发展，使研究人员得以在复杂数据集下高效识别目标信号。诸多具有时间紧迫性的物种保护需求均可从这类方法中获益良多。我们开发并实证评估了多款深度神经网络，用于检测濒危北大西洋露脊鲸（*Eubalaena glacialis*）的发声信号。我们将这些深度架构的检测性能，与针对该物种核心发声——上行发声（upcall）——的传统检测算法开展了对比实验。研究结果显示，深度学习架构可将误报率降低数个数量级，同时大幅提升信号检测的召回能力。我们证实，仅基于单一地理区域数天内的录音训练得到的深度神经网络，能够很好地泛化至多年跨度以及该物种全分布范围的声学数据；且极低的误报率使得该算法的输出结果可便捷开展质控验证工作。我们开发的深度神经网络可依托现有软件便捷部署，有望为濒危物种保护研究提供全新的研究视角与技术支撑。2020 31953462 PMC6969184 NMFS (National Marine Fisheries Service) NEFSC (Northeast Fisheries Science Center) PMC https://dx.doi.org/10.1038/s41598-020-57549-y CC BY 1955