257 citations · 415 across the 23 of their papers we have counts for
24 papers
Optimal Design of Shaped Pupil Lyot Coronagraph for China's Large Segmented Telescope
Kaining Yang, He Sun, Bin B. Ren +7
China's next-generation large telescopes, such as the Expandable Aperture Segmented Telescope (EAST) and the Large Optical/Infrared Telescope (LOT), are being planned as flagship f…
Large Language Continuous Diffusion Models
Zhihan Yang, Wei Guo, Jean-Marie Lemercier +14
Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and…
DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
Jean-Marie Lemercier, Tomas Geffner, Karsten Kreis +3
Diffusion language models intrinsically fail to capture correlations between decoded tokens, which leads to a harsh trade-off between sampling quality and throughput. To solve this…
Scaling Beyond Masked Diffusion Language Models
Subham Sekhar Sahoo, Jean-Marie Lemercier, Zhihan Yang +4
Diffusion language models are a promising alternative to autoregressive models due to their potential for faster generation. Among discrete diffusion approaches, Masked diffusion c…
Discrete Diffusion for Generative Modeling of Text-Aligned Speech Tokens
Pin-Jui Ku, He Huang, Jean-Marie Lemercier +3
This paper introduces a discrete diffusion model (DDM) framework for text-aligned speech tokenization and reconstruction. By replacing the auto-regressive speech decoder with a dis…
HRTF Estimation using a Score-based Prior
Etienne Thuillier, Jean-Marie Lemercier, Eloi Moliner +2
We present a head-related transfer function (HRTF) estimation method which relies on a data-driven prior given by a score-based diffusion model. The HRTF is estimated in reverberan…
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…
Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models
Jean-Marie Lemercier, Eloi Moliner, Simon Welker +2
This paper presents an unsupervised method for single-channel blind dereverberation and room impulse response (RIR) estimation, called BUDDy. The algorithm is rooted in Bayesian po…
An Independence-promoting Loss for Music Generation with Language Models
Jean-Marie Lemercier, Simon Rouard, Jade Copet +2
Music generation schemes using language modeling rely on a vocabulary of audio tokens, generally provided as codes in a discrete latent space learnt by an auto-encoder. Multi-stage…
BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion Models
Eloi Moliner, Jean-Marie Lemercier, Simon Welker +2
In this paper, we present an unsupervised single-channel method for joint blind dereverberation and room impulse response estimation, based on posterior sampling with diffusion mod…
Diffusion Models for Audio Restoration
Jean-Marie Lemercier, Julius Richter, Simon Welker +3
With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for…
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…
Diffusion Posterior Sampling for Informed Single-Channel Dereverberation
Jean-Marie Lemercier, Simon Welker, Timo Gerkmann
We present in this paper an informed single-channel dereverberation method based on conditional generation with diffusion models. With knowledge of the room impulse response, the a…
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…
Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model
Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning +1
In this paper we present a method for single-channel wind noise reduction using our previously proposed diffusion-based stochastic regeneration model combining predictive and gener…
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…
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation
Jean-Marie Lemercier, Julian Tobergte, Timo Gerkmann
In this paper, we present a scheme for extending deep neural network-based multiplicative maskers to deep subband filters for speech restoration in the time-frequency domain. The r…
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…
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…
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…
Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices
Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning +1
This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power s…
A neural network-supported two-stage algorithm for lightweight dereverberation on hearing devices
Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning +1
A two-stage lightweight online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filter with a sing…
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments
Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning +1
In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman filtering variant of the weighted prediction error (WPE) method is propose…
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network
Xiaolin Hu, Kai Li, Weiyi Zhang +3
Recent advances in the design of neural network architectures, in particular those specialized in modeling sequences, have provided significant improvements in speech separation pe…