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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13029 |
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| _version_ | 1866914122948411392 |
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| author | Xu, Nan Huang, Zhaolong Zhi, Xiaonan |
| author_facet | Xu, Nan Huang, Zhaolong Zhi, Xiaonan |
| contents | With the development of deep learning, speech enhancement has been greatly optimized in terms of speech quality. Previous methods typically focus on the discriminative supervised learning or generative modeling, which tends to introduce speech distortions or high computational cost. In this paper, we propose MDDM, a Multi-view Discriminative enhanced Diffusion-based Model. Specifically, we take the features of three domains (time, frequency and noise) as inputs of a discriminative prediction network, generating the preliminary spectrogram. Then, the discriminative output can be converted to clean speech by several inference sampling steps. Due to the intersection of the distributions between discriminative output and clean target, the smaller sampling steps can achieve the competitive performance compared to other diffusion-based methods. Experiments conducted on a public dataset and a realworld dataset validate the effectiveness of MDDM, either on subjective or objective metric. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MDDM: A Multi-view Discriminative Enhanced Diffusion-based Model for Speech Enhancement Xu, Nan Huang, Zhaolong Zhi, Xiaonan Audio and Speech Processing With the development of deep learning, speech enhancement has been greatly optimized in terms of speech quality. Previous methods typically focus on the discriminative supervised learning or generative modeling, which tends to introduce speech distortions or high computational cost. In this paper, we propose MDDM, a Multi-view Discriminative enhanced Diffusion-based Model. Specifically, we take the features of three domains (time, frequency and noise) as inputs of a discriminative prediction network, generating the preliminary spectrogram. Then, the discriminative output can be converted to clean speech by several inference sampling steps. Due to the intersection of the distributions between discriminative output and clean target, the smaller sampling steps can achieve the competitive performance compared to other diffusion-based methods. Experiments conducted on a public dataset and a realworld dataset validate the effectiveness of MDDM, either on subjective or objective metric. |
| title | MDDM: A Multi-view Discriminative Enhanced Diffusion-based Model for Speech Enhancement |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.13029 |