Automatic detection and classification system based on convolutional neural networks for bearded seal vocalizations in Alaska

Abstract

Bearded seal vocalizations were recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016–2017. Bearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. An automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. The DCS is divided into two sections. First, regions of interest (ROI) containing potential bearded seal calls are detected through a 2D normalized cross-correlation of the measured spectrogram and a representative template of the two calls of interest considered in this work. Second, CNNs are used to classify the ROIs to determine if they are noise or a specified vocalization. The CNNs are trained on 80% of the ROIs manually labeled from one of the recorders and validated on the remaining 20% with a classification accuracy above 95.5%. When assessing the generalization performance of the DCS, we tested on the remaining three recorders located at different positions, obtaining a precision above 89.2% for the main class of the two types of calls. This study provides evidence that CNNs are suitable for classifying bearded seal vocalizations from ROIs found by classic detection techniques. [Work supported by ONR via Grant No. N00014-18-1-2140.]