257 citations · 424 across the 25 of their papers we have counts for
6 papers · 1 filter
Single and Few-step Diffusion for Generative Speech Enhancement
Bunlong Lay, Jean-Marie Lemercier, Julius Richter +1
Diffusion models have shown promising results in speech enhancement, using a task-adapted diffusion process for the conditional generation of clean speech given a noisy mixture. Ho…
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings
Danilo de Oliveira, Julius Richter, Jean-Marie Lemercier +2
Since its inception, the field of deep speech enhancement has been dominated by predictive (discriminative) approaches, such as spectral mapping or masking. Recently, however, nove…
Audio-Visual Speech Enhancement with Score-Based Generative Models
Julius Richter, Simone Frintrop, Timo Gerkmann
This paper introduces an audio-visual speech enhancement system that leverages score-based generative models, also known as diffusion models, conditioned on visual information. In…
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
Héctor Martel, Julius Richter, Kai Li +2
We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separati…
Speech Signal Improvement Using Causal Generative Diffusion Models
Julius Richter, Simon Welker, Jean-Marie Lemercier +3
In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions. The method is based on a generative diffusion model w…
Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement
Bunlong Lay, Simon Welker, Julius Richter +1
Recently, score-based generative models have been successfully employed for the task of speech enhancement. A stochastic differential equation is used to model the iterative forwar…