Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement
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
Code (1)
Tasks
Speech EnhancementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition
In this paper, we propose a variance-preserving interpolation framework to improve diffusion models for single-channel speech enhancement (SE) and automatic speech recognition (ASR). This new variance-preserving interpol…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Speech Enhancementspeech-recognition+1An Analysis of the Variance of Diffusion-based Speech Enhancement
Diffusion models proved to be powerful models for generative speech enhancement. In recent SGMSE+ approaches, training involves a stochastic differential equation for the diffusion process, adding both Gaussian and envir…
Speech EnhancementNormalize Everything: A Preconditioned Magnitude-Preserving Architecture for Diffusion-Based Speech Enhancement
This paper presents a new framework for diffusion-based speech enhancement. Our method employs a Schroedinger bridge to transform the noisy speech distribution into the clean speech distribution. To stabilize and improve…
Image GenerationSpeech EnhancementFrame Interpolation with Consecutive Brownian Bridge Diffusion
Recent work in Video Frame Interpolation (VFI) tries to formulate VFI as a diffusion-based conditional image generation problem, synthesizing the intermediate frame given a random noise and neighboring frames. Due to the…
Conditional Image GenerationImage GenerationVideo Frame InterpolationArtiFree: Detecting and Reducing Generative Artifacts in Diffusion-based Speech Enhancement
Diffusion-based speech enhancement (SE) achieves natural-sounding speech and strong generalization, yet suffers from key limitations like generative artifacts and high inference latency. In this work, we systematically s…
Speech Enhancement