257 citations · 969 across the 53 of their papers we have counts for
20 papers · 1 filter
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation
Jean-Marie Lemercier, Julius Richter, Simon Welker +1
Diffusion models have shown a great ability at bridging the performance gap between predictive and generative approaches for speech enhancement. We have shown that they may even ou…
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models
Huajian Fang, Timo Gerkmann
Single-channel deep speech enhancement approaches often estimate a single multiplicative mask to extract clean speech without a measure of its accuracy. Instead, in this work, we p…
DriftRec: Adapting diffusion models to blind JPEG restoration
Simon Welker, Henry N. Chapman, Timo Gerkmann
In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels. We propose an elegant modification o…
DiffPhase: Generative Diffusion-based STFT Phase Retrieval
Tal Peer, Simon Welker, Timo Gerkmann
Diffusion probabilistic models have been recently used in a variety of tasks, including speech enhancement and synthesis. As a generative approach, diffusion models have been shown…
Spatially Selective Deep Non-linear Filters for Speaker Extraction
Kristina Tesch, Timo Gerkmann
In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work,…
Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration
Jean-Marie Lemercier, Julius Richter, Simon Welker +1
Diffusion-based generative models have had a high impact on the computer vision and speech processing communities these past years. Besides data generation tasks, they have also be…