9 papers
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
Simon Welker, Lorenz Kuger, Tim Roith +4
In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions…
Real-Time Streamable Generative Speech Restoration with Flow Matching
Simon Welker, Bunlong Lay, Maris Hillemann +2
Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and spawning a new research direction. Their…
Diffusion Buffer for Online Generative Speech Enhancement
Bunlong Lay, Rostislav Makarov, Simon Welker +2
Online Speech Enhancement was mainly reserved for predictive models. A key advantage of these models is that for an incoming signal frame from a stream of data, the model is called…
Speech Enhancement and Dereverberation with Diffusion-based Generative Models
Julius Richter, Simon Welker, Jean-Marie Lemercier +2
In this work, we build upon our previous publication and use diffusion-based generative models for speech enhancement. We present a detailed overview of the diffusion process that…
Real-Time Streaming Mel Vocoding with Generative Flow Matching
Simon Welker, Tal Peer, Timo Gerkmann
The task of Mel vocoding, i.e., the inversion of a Mel magnitude spectrogram to an audio waveform, is still a key component in many text-to-speech (TTS) systems today. Based on gen…
Non-intrusive Speech Quality Assessment with Diffusion Models Trained on Clean Speech
Danilo de Oliveira, Julius Richter, Jean-Marie Lemercier +2
Diffusion models have found great success in generating high quality, natural samples of speech, but their potential for density estimation for speech has so far remained largely u…