Beluga whale acoustic signal classification using deep learning neural network models.

Abstract

Over a decade after the Cook Inlet beluga (Delphinapterus leucas) was listed as endangered in 2008, the population has shown no sign of recovery. Lack of ecological knowledge limits the understanding of, and ability to manage, potential threats impeding recovery of this declining population. National Oceanic and Atmospheric Administration Fisheries, in partnership with the Alaska Department of Fish and Game, initiated a passive acoustics monitoring program in 2017 to investigate beluga seasonal occurrence by deploying a series of passive acoustic moorings. Data have been processed with semi-automated tonal detectors followed by time intensive manual validation. To reduce this labor intensive and time-consuming process, in addition to increasing the accuracy of classification results, the authors constructed an ensembled deep learning convolutional neural network model to classify beluga detections as true or false. Using a 0.5 threshold, the final model achieves 96.57% precision and 92.26% recall on testing dataset. This methodology proves to be successful at classifying beluga signals, and the framework can be easily generalized to other acoustic classification problems.