Automatic detection and classification of beluga whale calls in the St. Lawrence estuary

Abstract

The endangered beluga whale (Delphinapterus leucas) of the St. Lawrence Estuary (SLEB) faces threats from a variety of anthropogenic factors. Since belugas are a highly social and vocal species, passive acoustic monitoring has the potential to deliver, in a non-invasive and continuous way, real-time information on SLEB spatiotemporal habitat use, which is crucial for their monitoring and conservation. In this study, we introduce an automatic pipeline to analyze continuous passive acoustic data and provide standard and accurate estimations of SLEB acoustic presence and vocal activity. An object detector extracted vocalizations of beluga whales from an acoustic recording of beluga vocal activity. Then, two deep learning classifiers discriminated between high-frequency call types (40–120 kHz) and the presence of low-frequency components (0–20 kHz), respectively. Different algorithms were tested for each step and their main combinations were compared in time and performance. We focused our work on a high residency area, Baie Sainte-Marguerite (BSM), used for socialization and feeding by SLEB. Overall, this project showed that accurate continuous analysis of SLEB vocal activity at BSM could provide valuable information to estimate habitat use, link beluga behavior and acoustic activity within and between herds, and quantify beluga presence and abundance.