Diffusion-based speech enhancement with a weighted generative-supervised learning loss
Diffusion-based generative models have recently gained attention in speech enhancement (SE), providing an alternative to conventional supervised methods. These models transform clean speech training samples into Gaussian noise centered at noisy speech, and subsequently learn a parameterized model to reverse this process, conditionally on noisy speech. Unlike supervised methods, generative-based SE approaches usually rely solely on an unsupervised loss, which may result in less efficient incorporation of conditioned noisy speech. To address this issue, we propose augmenting the original diffusion training objective with a mean squared error (MSE) loss, measuring the discrepancy between estimated enhanced speech and ground-truth clean speech at each reverse process iteration. Experimental results demonstrate the effectiveness of our proposed methodology.
Code (0)
등록된 구현이 없습니다.
Tasks
Speech EnhancementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unsupervised 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 EnhancementDiffusion-based Unsupervised Audio-visual Speech Enhancement
This paper proposes a new unsupervised audio-visual speech enhancement (AVSE) approach that combines a diffusion-based audio-visual speech generative model with a non-negative matrix factorization (NMF) noise model. Firs…
Speech 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 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 EnhancementA weighted-variance variational autoencoder model for speech enhancement
We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued Gaussian distribution is usually assumed …
Speech Enhancement