An open-source acoustic detector for beluga whales, with evaluations in the Western Canadian Arctic

Belugas (Delphinapterus leucas) face threats from various sources, including noise from oil and gas exploration, vessel traffic, and other human activities. Here we present the development of a deep learning-based acoustic detector to automatically detect the species, and measure its performance when applied to study sites in the western Canadian Arctic. We used over 20,000 individually annotated beluga vocalizations to train deep learning models in the binary task of classifying 3-second audio clips into containing beluga vocalizations or not. Approximately 7,000 annotated vocalizations were reserved for testing, and models were evaluated on their ability to correctly label audio clips of two lengths: 3 s and 60 s. The average F1 score (across 10 models) on 3 s clips was 0.82 with a standard deviation of 0.027, with the best model achieving 0.86. When applied to 60 s clips, the best model achieved an F1 score of 0.96. We used the trained classifier to build a detector that processes longer recordings and will make it available as an open-source tool.