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Found 6 papers, 3 papers with code
An Analysis of the Variance of Diffusion-based Speech Enhancement
The speech enhancement performance varies depending on the choice of the stochastic differential equation that controls the evolution of the mean and the variance along the diffusion processes when adding environmental and Gaussian noise.
Single and Few-step Diffusion for Generative Speech Enhancement
While the performance of usual generative diffusion algorithms drops dramatically when lowering the number of function evaluations (NFEs) to obtain single-step diffusion, we show that our proposed method keeps a steady performance and therefore largely outperforms the diffusion baseline in this setting and also generalizes better than its predictive counterpart.
EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data
Subsequently, at inference, a target emotion embedding is employed to convert the emotion of the input utterance to the given target emotion.
Speech Signal Improvement Using Causal Generative Diffusion Models
In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions.
Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement
Recently, score-based generative models have been successfully employed for the task of speech enhancement.
Speech Enhancement and Dereverberation with Diffusion-based Generative Models
This matches our forward process which moves from clean speech to noisy speech by including a drift term.
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Speech Enhancement
on VoiceBank + DEMAND
