Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914226330664960 |
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| author | Kim, Euiyeon Choi, Yong-Hoon |
| author_facet | Kim, Euiyeon Choi, Yong-Hoon |
| contents | We introduce a new music source separation model tailored for accurate vocal isolation. Unlike Transformer-based approaches, which often fail to capture intermittently occurring vocals, our model leverages Mamba2, a recent state space model, to better capture long-range temporal dependencies. To handle long input sequences efficiently, we combine a band-splitting strategy with a dual-path architecture. Experiments show that our approach outperforms recent state-of-the-art models, achieving a cSDR of 11.03 dB-the best reported to date-and delivering substantial gains in uSDR. Moreover, the model exhibits stable and consistent performance across varying input lengths and vocal occurrence patterns. These results demonstrate the effectiveness of Mamba-based models for high-resolution audio processing and open up new directions for broader applications in audio research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions Kim, Euiyeon Choi, Yong-Hoon Sound Artificial Intelligence Audio and Speech Processing We introduce a new music source separation model tailored for accurate vocal isolation. Unlike Transformer-based approaches, which often fail to capture intermittently occurring vocals, our model leverages Mamba2, a recent state space model, to better capture long-range temporal dependencies. To handle long input sequences efficiently, we combine a band-splitting strategy with a dual-path architecture. Experiments show that our approach outperforms recent state-of-the-art models, achieving a cSDR of 11.03 dB-the best reported to date-and delivering substantial gains in uSDR. Moreover, the model exhibits stable and consistent performance across varying input lengths and vocal occurrence patterns. These results demonstrate the effectiveness of Mamba-based models for high-resolution audio processing and open up new directions for broader applications in audio research. |
| title | Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.14556 |