A bioacoustic 'agile modeling' system, built from frozen bird-song embeddings, vector search, and active learning, produced high-AUC recognizers for Hawaiian honeycreepers, coral-reef fish sound types, and Christmas Island birds with human review times of minutes to hours.
AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring
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abstract
Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires the identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources can be found on our GitHub repository https://github.com/soundclim/anuraset.
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The Search for Squawk: Agile Modeling in Bioacoustics
A bioacoustic 'agile modeling' system, built from frozen bird-song embeddings, vector search, and active learning, produced high-AUC recognizers for Hawaiian honeycreepers, coral-reef fish sound types, and Christmas Island birds with human review times of minutes to hours.