Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement

• URL: http://arxiv.org/abs/2306.08527v2
• Date: Sun, 17 Sep 2023 13:27:18 GMT
• Title: Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement
• Authors: Zilu Guo, Jun Du, Chin-Hui Lee, Yu Gao, Wenbin Zhang
• Abstract summary: We present a framework that encapsulates both the VP- and variance-exploding (VE)-based diffusion methods. To improve performance and ease model training, we analyze the common difficulties encountered in diffusion models. We evaluate our model against several methods using a public benchmark to showcase the effectiveness of our approach.
• Score: 53.2171981279647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of this study is to implement diffusion models for speech enhancement (SE). The first step is to emphasize the theoretical foundation of variance-preserving (VP)-based interpolation diffusion under continuous conditions. Subsequently, we present a more concise framework that encapsulates both the VP- and variance-exploding (VE)-based interpolation diffusion methods. We demonstrate that these two methods are special cases of the proposed framework. Additionally, we provide a practical example of VP-based interpolation diffusion for the SE task. To improve performance and ease model training, we analyze the common difficulties encountered in diffusion models and suggest amenable hyper-parameters. Finally, we evaluate our model against several methods using a public benchmark to showcase the effectiveness of our approach

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