MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914066574868480 |
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| author | Zhu, Yike Kang, Boyi Wang, Ziqian Li, Xingchen Zhang, Zihan Li, Wenjie Xiao, Longshuai Xue, Wei Xie, Lei |
| author_facet | Zhu, Yike Kang, Boyi Wang, Ziqian Li, Xingchen Zhang, Zihan Li, Wenjie Xiao, Longshuai Xue, Wei Xie, Lei |
| contents | Speech enhancement (SE) recovers clean speech from noisy signals and is vital for applications such as telecommunications and automatic speech recognition (ASR). While generative approaches achieve strong perceptual quality, they often rely on multi-step sampling (diffusion/flow-matching) or large language models, limiting real-time deployment. To mitigate these constraints, we present MeanFlowSE, a one-step generative SE framework. It adopts MeanFlow to predict an average-velocity field for one-step latent refinement and conditions the model on self-supervised learning (SSL) representations rather than VAE latents. This design accelerates inference and provides robust acoustic-semantic guidance during training. In the Interspeech 2020 DNS Challenge blind test set and simulated test set, MeanFlowSE attains state-of-the-art (SOTA) level perceptual quality and competitive intelligibility while significantly lowering both real-time factor (RTF) and model size compared with recent generative competitors, making it suitable for practical use. The code will be released upon publication at https://github.com/Hello3orld/MeanFlowSE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23299 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow Zhu, Yike Kang, Boyi Wang, Ziqian Li, Xingchen Zhang, Zihan Li, Wenjie Xiao, Longshuai Xue, Wei Xie, Lei Sound Audio and Speech Processing Speech enhancement (SE) recovers clean speech from noisy signals and is vital for applications such as telecommunications and automatic speech recognition (ASR). While generative approaches achieve strong perceptual quality, they often rely on multi-step sampling (diffusion/flow-matching) or large language models, limiting real-time deployment. To mitigate these constraints, we present MeanFlowSE, a one-step generative SE framework. It adopts MeanFlow to predict an average-velocity field for one-step latent refinement and conditions the model on self-supervised learning (SSL) representations rather than VAE latents. This design accelerates inference and provides robust acoustic-semantic guidance during training. In the Interspeech 2020 DNS Challenge blind test set and simulated test set, MeanFlowSE attains state-of-the-art (SOTA) level perceptual quality and competitive intelligibility while significantly lowering both real-time factor (RTF) and model size compared with recent generative competitors, making it suitable for practical use. The code will be released upon publication at https://github.com/Hello3orld/MeanFlowSE. |
| title | MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.23299 |