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What Matters for Bioacoustic Encoding

Created by
  • Haebom

Author

Marius Miron, David Robinson, Milad Alizadeh, Ellen Gilsenan-McMahon, Gagan Narula, Emmanuel Chemla, Maddie Cusimano, Felix Effenberger, Masato Hagiwara, Benjamin Hoffman, Sara Keen, Diane Kim, Jane Lawton, Jen-Yu Liu, Aza Raskin, Olivier Pietquin, Matthieu Geist

Outline

This paper presents a large-scale experimental study proposing a general-purpose bioacoustic encoder capable of extracting representations useful for diverse subtasks in bioacoustics. To overcome the limitations of previous studies, which focus on specific species (primarily birds), rely on a single model architecture or training method, and are evaluated on a limited number of tasks and datasets, we comprehensively examined training data diversity and scale, model architecture and training methods, evaluation tasks, and dataset breadth. Using 26 datasets and a variety of tasks, including species classification, detection, entity identification, and vocal repertoire discovery, we found that supervised pre-training on a mixed bioacoustic and general audio corpus, followed by supervised training, improved both in-distribution and out-of-distribution performance. Furthermore, we demonstrated the importance of data diversity in both stages. We publicly disclose model checkpoints that achieved state-of-the-art performance to support ongoing research and applications.

Takeaways, Limitations

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Takeaways:
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Development and performance verification of a general-purpose encoder applicable to various bioacoustic tasks.
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Demonstrating the effectiveness of self-supervised learning pre-training and supervised learning supplementary training using mixed datasets.
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Emphasize the importance of data diversity and suggest optimal training strategies.
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Supporting research and applications through the release of model checkpoints that achieve cutting-edge performance.
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Limitations:
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Lack of specific description of the type and size of the dataset used in the study.
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Absence of comparative analysis with other general-purpose audio encoders.
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Further validation of generalization performance for specific species or environments is needed.
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Inadequate assessment of the model's adaptability to long-term data changes.
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