257 citations · 257 across the 12 of their papers we have counts for
13 papers
A Fast Solver for Interpolating Stochastic Differential Equation Diffusion Models for Speech Restoration
Bunlong Lay, Timo Gerkmann
Diffusion Probabilistic Models (DPMs) are a well-established class of diffusion models for unconditional image generation, while SGMSE+ is a well-established conditional diffusion…
Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models
Sina Khanagha, Bunlong Lay, Timo Gerkmann
Single-channel speech enhancement models face significant performance degradation in extremely noisy environments. While prior work has shown that complementary bone-conducted spee…
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…
Diffusion Buffer: Online Diffusion-based Speech Enhancement with Sub-Second Latency
Bunlong Lay, Rostislav Makarov, Timo Gerkmann
Diffusion models are a class of generative models that have been recently used for speech enhancement with remarkable success but are computationally expensive at inference time. T…
Robustness of Speech Separation Models for Similar-pitch Speakers
Bunlong Lay, Sebastian Zaczek, Kristina Tesch +1
Single-channel speech separation is a crucial task for enhancing speech recognition systems in multi-speaker environments. This paper investigates the robustness of state-of-the-ar…
EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation
Julius Richter, Yi-Chiao Wu, Steven Krenn +5
We release the EARS (Expressive Anechoic Recordings of Speech) dataset, a high-quality speech dataset comprising 107 speakers from diverse backgrounds, totaling in 100 hours of cle…
An Analysis of the Variance of Diffusion-based Speech Enhancement
Bunlong Lay, Timo Gerkmann
Diffusion models proved to be powerful models for generative speech enhancement. In recent SGMSE+ approaches, training involves a stochastic differential equation for the diffusion…
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…
EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data
Navin Raj Prabhu, Bunlong Lay, Simon Welker +2
Speech emotion conversion is the task of converting the expressed emotion of a spoken utterance to a target emotion while preserving the lexical content and speaker identity. While…
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…
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…