BioME: A Resource-Efficient Bioacoustic Foundational Model for IoT Applications
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arXiv
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| Format: | Preprint |
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2026
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| author | Guimarães, Heitor R. Tiwari, Abhishek Abdollahi, Mahsa Avila, Anderson R. Falk, Tiago H. |
| author_facet | Guimarães, Heitor R. Tiwari, Abhishek Abdollahi, Mahsa Avila, Anderson R. Falk, Tiago H. |
| contents | Passive acoustic monitoring has become a key strategy in biodiversity assessment, conservation, and behavioral ecology, especially as Internet-of-Things (IoT) devices enable continuous in situ audio collection at scale. While recent self-supervised learning (SSL)-based audio encoders, such as BEATs and AVES, have shown strong performance in bioacoustic tasks, their computational cost and limited robustness to unseen environments hinder deployment on resource-constrained platforms. In this work, we introduce BioME, a resource-efficient audio encoder designed for bioacoustic applications. BioME is trained via layer-to-layer distillation from a high-capacity teacher model, enabling strong representational transfer while reducing the parameter count by 75%. To further improve ecological generalization, the model is pretrained on multi-domain data spanning speech, environmental sounds, and animal vocalizations. A key contribution is the integration of modulation-aware acoustic features via FiLM conditioning, injecting a DSP-inspired inductive bias that enhances feature disentanglement in low-capacity regimes. Across multiple bioacoustic tasks, BioME matches or surpasses the performance of larger models, including its teacher, while being suitable for resource-constrained IoT deployments. For reproducibility, code and pretrained checkpoints are publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09970 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BioME: A Resource-Efficient Bioacoustic Foundational Model for IoT Applications Guimarães, Heitor R. Tiwari, Abhishek Abdollahi, Mahsa Avila, Anderson R. Falk, Tiago H. Audio and Speech Processing Sound Passive acoustic monitoring has become a key strategy in biodiversity assessment, conservation, and behavioral ecology, especially as Internet-of-Things (IoT) devices enable continuous in situ audio collection at scale. While recent self-supervised learning (SSL)-based audio encoders, such as BEATs and AVES, have shown strong performance in bioacoustic tasks, their computational cost and limited robustness to unseen environments hinder deployment on resource-constrained platforms. In this work, we introduce BioME, a resource-efficient audio encoder designed for bioacoustic applications. BioME is trained via layer-to-layer distillation from a high-capacity teacher model, enabling strong representational transfer while reducing the parameter count by 75%. To further improve ecological generalization, the model is pretrained on multi-domain data spanning speech, environmental sounds, and animal vocalizations. A key contribution is the integration of modulation-aware acoustic features via FiLM conditioning, injecting a DSP-inspired inductive bias that enhances feature disentanglement in low-capacity regimes. Across multiple bioacoustic tasks, BioME matches or surpasses the performance of larger models, including its teacher, while being suitable for resource-constrained IoT deployments. For reproducibility, code and pretrained checkpoints are publicly available. |
| title | BioME: A Resource-Efficient Bioacoustic Foundational Model for IoT Applications |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2602.09970 |