Vector Quantized Diffusion Model Based Speech Bandwidth Extension
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916393589407744 |
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| author | Fang, Yuan Bai, Jinglin Wang, Jiajie Zhang, Xueliang |
| author_facet | Fang, Yuan Bai, Jinglin Wang, Jiajie Zhang, Xueliang |
| contents | Recent advancements in neural audio codec (NAC) unlock new potential in audio signal processing. Studies have increasingly explored leveraging the latent features of NAC for various speech signal processing tasks. This paper introduces the first approach to speech bandwidth extension (BWE) that utilizes the discrete features obtained from NAC. By restoring high-frequency details within highly compressed discrete tokens, this approach enhances speech intelligibility and naturalness. Based on Vector Quantized Diffusion, the proposed framework combines the strengths of advanced NAC, diffusion models, and Mamba-2 to reconstruct high-frequency speech components. Extensive experiments demonstrate that this method exhibits superior performance across both log-spectral distance and ViSQOL, significantly improving speech quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05784 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vector Quantized Diffusion Model Based Speech Bandwidth Extension Fang, Yuan Bai, Jinglin Wang, Jiajie Zhang, Xueliang Sound Audio and Speech Processing Recent advancements in neural audio codec (NAC) unlock new potential in audio signal processing. Studies have increasingly explored leveraging the latent features of NAC for various speech signal processing tasks. This paper introduces the first approach to speech bandwidth extension (BWE) that utilizes the discrete features obtained from NAC. By restoring high-frequency details within highly compressed discrete tokens, this approach enhances speech intelligibility and naturalness. Based on Vector Quantized Diffusion, the proposed framework combines the strengths of advanced NAC, diffusion models, and Mamba-2 to reconstruct high-frequency speech components. Extensive experiments demonstrate that this method exhibits superior performance across both log-spectral distance and ViSQOL, significantly improving speech quality. |
| title | Vector Quantized Diffusion Model Based Speech Bandwidth Extension |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.05784 |