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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.12534 |
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| _version_ | 1866913125594300416 |
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| author | Song, Tianyu Ta, Ton Viet Thamwattana, Ngamta Nomura, Hisako Nguyen, Linh Thi Hoai |
| author_facet | Song, Tianyu Ta, Ton Viet Thamwattana, Ngamta Nomura, Hisako Nguyen, Linh Thi Hoai |
| contents | Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds. To fill this gap, we adapt speech enhancement methods and build BioSEN, a model made for bioacoustic signals. BioSEN has three modules: a multi-scale dual-axis attention unit for time-frequency feature extraction, a bio-harmonic multi-scale enhancement unit for capturing harmonic structures, and an
energy-adaptive gating connection unit that uses frequency weights to keep vocalizations from being removed as noise. Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation. These results show BioSEN's strength for bioacoustic audio enhancement and its promise for biodiversity monitoring and conservation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12534 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations Song, Tianyu Ta, Ton Viet Thamwattana, Ngamta Nomura, Hisako Nguyen, Linh Thi Hoai Sound Machine Learning Neurons and Cognition Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds. To fill this gap, we adapt speech enhancement methods and build BioSEN, a model made for bioacoustic signals. BioSEN has three modules: a multi-scale dual-axis attention unit for time-frequency feature extraction, a bio-harmonic multi-scale enhancement unit for capturing harmonic structures, and an energy-adaptive gating connection unit that uses frequency weights to keep vocalizations from being removed as noise. Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation. These results show BioSEN's strength for bioacoustic audio enhancement and its promise for biodiversity monitoring and conservation. |
| title | BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations |
| topic | Sound Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2605.12534 |