Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement
This work addresses speech enhancement for audio processing applications, presenting an incremental improvement by refining diffusion model frameworks.
The study tackled speech enhancement by implementing diffusion models, proposing a framework that unifies variance-preserving and variance-exploding interpolation methods, and achieved improved performance with optimized hyperparameters on a public benchmark.
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