State Space Models for Bioacoustics: A Comparative Evaluation with Transformers
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914487636852736 |
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| author | Tang, Chengyu Baskiyar, Sanjeev |
| author_facet | Tang, Chengyu Baskiyar, Sanjeev |
| contents | In this study, we evaluate the efficacy of the Mamba architecture bioacoustics by introducing BioMamba, a Mamba-based audio representation model for wildlife sounds. We pre-train a BioMamba using self-supervised learning on a large audio corpus and evaluate it on the BEANS benchmark across diverse classification and detection tasks. Compared to the state-of-the-art Transformer-based model (AVES), BioMamba achieves comparable performance while significantly reducing VRAM consumption. Our results demonstrate Mamba's potential as a computationally efficient alternative for real-world environmental monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | State Space Models for Bioacoustics: A Comparative Evaluation with Transformers Tang, Chengyu Baskiyar, Sanjeev Sound Artificial Intelligence In this study, we evaluate the efficacy of the Mamba architecture bioacoustics by introducing BioMamba, a Mamba-based audio representation model for wildlife sounds. We pre-train a BioMamba using self-supervised learning on a large audio corpus and evaluate it on the BEANS benchmark across diverse classification and detection tasks. Compared to the state-of-the-art Transformer-based model (AVES), BioMamba achieves comparable performance while significantly reducing VRAM consumption. Our results demonstrate Mamba's potential as a computationally efficient alternative for real-world environmental monitoring. |
| title | State Space Models for Bioacoustics: A Comparative Evaluation with Transformers |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2512.03563 |