SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915856924016640 |
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| author | Lee, Yongjoon Choi, Jung-Woo |
| author_facet | Lee, Yongjoon Choi, Jung-Woo |
| contents | General speech restoration demands techniques that can interpret complex speech structures under various distortions. While State-Space Models like SEMamba have advanced the state-of-the-art in speech denoising, they are not inherently optimized for critical speech characteristics, such as spectral periodicity or multi-resolution frequency analysis. In this work, we introduce an architecture tailored to incorporate speech-specific features as inductive biases. In particular, we propose Frequency GLP, a frequency feature extraction block that effectively and efficiently leverages the properties of frequency bins. Then, we design a multi-resolution parallel time-frequency dual-processing block to capture diverse spectral patterns, and a learnable mapping to further enhance model performance. With all our ideas combined, the proposed SEMamba++ achieves the best performance among multiple baseline models while remaining computationally efficient. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_11669 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns Lee, Yongjoon Choi, Jung-Woo Audio and Speech Processing Sound General speech restoration demands techniques that can interpret complex speech structures under various distortions. While State-Space Models like SEMamba have advanced the state-of-the-art in speech denoising, they are not inherently optimized for critical speech characteristics, such as spectral periodicity or multi-resolution frequency analysis. In this work, we introduce an architecture tailored to incorporate speech-specific features as inductive biases. In particular, we propose Frequency GLP, a frequency feature extraction block that effectively and efficiently leverages the properties of frequency bins. Then, we design a multi-resolution parallel time-frequency dual-processing block to capture diverse spectral patterns, and a learnable mapping to further enhance model performance. With all our ideas combined, the proposed SEMamba++ achieves the best performance among multiple baseline models while remaining computationally efficient. |
| title | SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2603.11669 |