Detecting Endangered Baleen Whales within Acoustic Recordings using R-CNNs

Research and development into automated systems that can detect the vocalizations of endangered species of whales within acoustic recordings is a difficult yet important task. Over the past several years, hundreds of deceased whales have washed ashore along the coasts of North America. In many cases the primary cause of death of these species has been directly linked to human activity including vessel collisions and entanglement in fishing gear. In this work, we introduce preliminary work towards developing an end-to-end detection system using a Region-based Convolutional Neural Network (R-CNN) trained on spectrogram representations of acoustic recordings and labelled bounding boxes around the vocalizations of three species of endangered baleen whales: blue, fin, and sei whales. In this way, the R-CNN can detect vocalizations in terms of both time and frequency against a background of ambient noise and other non-biological sources. The R-CNN can be used by stakeholders and policy makers to mitigate the risk of collisions and entanglements when the aforementioned species are detected in a given area.