Synthetic data enables context-aware bioacoustic sound event detection

• URL: http://arxiv.org/abs/2503.00296v1
• Date: Sat, 01 Mar 2025 02:03:22 GMT
• Title: Synthetic data enables context-aware bioacoustic sound event detection
• Authors: Benjamin Hoffman, David Robinson, Marius Miron, Vittorio Baglione, Daniela Canestrari, Damian Elias, Eva Trapote, Olivier Pietquin,
• Abstract summary: We propose a methodology for training foundation models that enhances their in-context learning capabilities.<n>We generate over 8.8 thousand hours of strongly-labeled audio and train a query-by-example, transformer-based model to perform few-shot bioacoustic sound event detection.<n>We make our trained model available via an API, to provide ecologists and ethologists with a training-free tool for bioacoustic sound event detection.
• Score: 18.158806322128527
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a methodology for training foundation models that enhances their in-context learning capabilities within the domain of bioacoustic signal processing. We use synthetically generated training data, introducing a domain-randomization-based pipeline that constructs diverse acoustic scenes with temporally strong labels. We generate over 8.8 thousand hours of strongly-labeled audio and train a query-by-example, transformer-based model to perform few-shot bioacoustic sound event detection. Our second contribution is a public benchmark of 13 diverse few-shot bioacoustics tasks. Our model outperforms previously published methods by 49%, and we demonstrate that this is due to both model design and data scale. We make our trained model available via an API, to provide ecologists and ethologists with a training-free tool for bioacoustic sound event detection.

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