MeanSE: Efficient Generative Speech Enhancement with Mean Flows
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909805993525248 |
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| author | Wang, Jiahe Wang, Hongyu Wang, Wei Yang, Lei Li, Chenda Zhang, Wangyou Tan, Lufen Qian, Yanmin |
| author_facet | Wang, Jiahe Wang, Hongyu Wang, Wei Yang, Lei Li, Chenda Zhang, Wangyou Tan, Lufen Qian, Yanmin |
| contents | Speech enhancement (SE) improves degraded speech's quality, with generative models like flow matching gaining attention for their outstanding perceptual quality. However, the flow-based model requires multiple numbers of function evaluations (NFEs) to achieve stable and satisfactory performance, leading to high computational load and poor 1-NFE performance. In this paper, we propose MeanSE, an efficient generative speech enhancement model using mean flows, which models the average velocity field to achieve high-quality 1-NFE enhancement. Experimental results demonstrate that our proposed MeanSE significantly outperforms the flow matching baseline with a single NFE, exhibiting extremely better out-of-domain generalization capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MeanSE: Efficient Generative Speech Enhancement with Mean Flows Wang, Jiahe Wang, Hongyu Wang, Wei Yang, Lei Li, Chenda Zhang, Wangyou Tan, Lufen Qian, Yanmin Audio and Speech Processing Speech enhancement (SE) improves degraded speech's quality, with generative models like flow matching gaining attention for their outstanding perceptual quality. However, the flow-based model requires multiple numbers of function evaluations (NFEs) to achieve stable and satisfactory performance, leading to high computational load and poor 1-NFE performance. In this paper, we propose MeanSE, an efficient generative speech enhancement model using mean flows, which models the average velocity field to achieve high-quality 1-NFE enhancement. Experimental results demonstrate that our proposed MeanSE significantly outperforms the flow matching baseline with a single NFE, exhibiting extremely better out-of-domain generalization capabilities. |
| title | MeanSE: Efficient Generative Speech Enhancement with Mean Flows |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.21214 |