The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin, Otilia Stretcu, Jenny Hamer, Lauren Harrell, Rob Laber, Hugo Larochelle, Bart van Merriënboer, Amanda Navine, Patrick Hart, Ben Williams, Timothy A. C. Lamont, Tries B. Razak, Mars Coral Restoration Team, Sheryn Brodie, Brendan Doohan, Phil Eichinski, Paul Roe, Lin Schwarzkopf, Tom Denton
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Passive acoustic monitoring (PAM) has shown great promise in helping ecologists understand the health of animal populations and ecosystems. However, extracting insights from millions of hours of audio recordings requires the development of specialized recognizers. This is typically a challenging task, necessitating large amounts of training data and machine learning expertise. In this work, we introduce a general, scalable and data-efficient system for developing recognizers for novel bioacoustic problems in under an hour. Our system consists of several key components that tackle problems in previous bioacoustic workflows: 1) highly generalizable acoustic embeddings pre-trained for birdsong classification minimize data hunger; 2) indexed audio search allows the efficient creation of classifier training datasets, and 3) precomputation of embeddings enables an efficient active learning loop, improving classifier quality iteratively with minimal wait time. Ecologists employed our system in three novel case studies: analyzing coral reef health through unidentified sounds; identifying juvenile Hawaiian bird calls to quantify breeding success and improve endangered species monitoring; and Christmas Island bird occupancy modeling. We augment the case studies with simulated experiments which explore the range of design decisions in a structured way and help establish best practices. Altogether these experiments showcase our system's scalability, efficiency, and generalizability, enabling scientists to quickly address new bioacoustic challenges.