Conditional Diffusion Probabilistic Model for Speech Enhancement
Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs. While generative models have shown strong potential in speech synthesis, they are still lagging behind in speech enhancement. This work leverages recent advances in diffusion probabilistic models, and proposes a novel speech enhancement algorithm that incorporates characteristics of the observed noisy speech signal into the diffusion and reverse processes. More specifically, we propose a generalized formulation of the diffusion probabilistic model named conditional diffusion probabilistic model that, in its reverse process, can adapt to non-Gaussian real noises in the estimated speech signal. In our experiments, we demonstrate strong performance of the proposed approach compared to representative generative models, and investigate the generalization capability of our models to other datasets with noise characteristics unseen during training.
Code (2)
Tasks
modelSpeech EnhancementSpeech SynthesisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models
Single-channel speech enhancement models face significant performance degradation in extremely noisy environments. While prior work has shown that complementary bone-conducted speech can guide enhancement, effective inte…
Speech EnhancementUnsupervised speech enhancement with diffusion-based generative models
Recently, conditional score-based diffusion models have gained significant attention in the field of supervised speech enhancement, yielding state-of-the-art performance. However, these methods may face challenges when g…
Speech EnhancementuSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models
Speech enhancement aims to improve the quality of speech signals in terms of quality and intelligibility, and speech editing refers to the process of editing the speech according to specific user needs. In this paper, we…
DenoisingSelf-Supervised LearningSpeech DenoisingSpeech EnhancementPosterior Transition Modeling for Unsupervised Diffusion-Based Speech Enhancement
We explore unsupervised speech enhancement using diffusion models as expressive generative priors for clean speech. Existing approaches guide the reverse diffusion process using noisy speech through an approximate, noise…
Speech EnhancementCold Diffusion for Speech Enhancement
Diffusion models have recently shown promising results for difficult enhancement tasks such as the conditional and unconditional restoration of natural images and audio signals. In this work, we explore the possibility o…
Speech Enhancement