Open-source deep learning models for acoustic detection and classification of orcas

Abstract

Current efforts to acoustically study and monitor killer whales in British Columbia (BC), including the endangered population of Southern Resident killer whales (SRKW), are hampered by the lack of sufficiently accurate sound detection and classification algorithms. Recently, several research groups have reported significant improvements in algorithm performance utilizing deep neural networks. However, for most practitioners, these novel tools remain out of reach. To bridge this gap, we are developing a set of open-source deep learning models for acoustic detection and classification of BC's killer whales. These models will be made publicly available along with expert-curated training and test sets to facilitate further development and applications. We are collaborating with Orcasound to deploy the models on their live data. We are also working together with the PAMGuard developer team to ensure that our models can be seamlessly imported and used in PAMGuard, a widely used open-source software platform for passive acoustic monitoring. In this contribution, we will provide an overview of our deep learning methodology and present preliminary results on model performance, with particular attention to the model's ability to handle diverse and variable acoustic environments and generalize to new “unseen” environments.