6 papers · 1 filter
U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model
Louis Bahrman, Marius Rodrigues, Mathieu Fontaine +1
This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to virtually unsupervised, relying sole…
The silence of the weights: a structural pruning strategy for attention-based audio signal architectures with second order metrics
Andrea Diecidue, Carlo Alberto Barbano, Piero Fraternali +2
Transformer-based models have become the state of the art across multiple domains, from natural language processing to machine listening, thanks to the attention mechanisms. Howeve…
Contrastive Knowledge Distillation for Embedding Refinement in Personalized Speech Enhancement
Thomas Serre, Mathieu Fontaine, Ãric Benhaim +1
Personalized speech enhancement (PSE) has shown convincing results when it comes to extracting a known target voice among interfering ones. The corresponding systems usually incorp…
GLA-Grad++: An Improved Griffin-Lim Guided Diffusion Model for Speech Synthesis
Teysir Baoueb, Xiaoyu Bie, Mathieu Fontaine +1
Recent advances in diffusion models have positioned them as powerful generative frameworks for speech synthesis, demonstrating substantial improvements in audio quality and stabili…
Speech dereverberation constrained on room impulse response characteristics
Louis Bahrman, Mathieu Fontaine, Jonathan Le Roux +1
Single-channel speech dereverberation aims at extracting a dry speech signal from a recording affected by the acoustic reflections in a room. However, most current deep learning-ba…
A lightweight dual-stage framework for personalized speech enhancement based on DeepFilterNet2
Thomas Serre, Mathieu Fontaine, Ãric Benhaim +2
Isolating the desired speaker's voice amidst multiplespeakers in a noisy acoustic context is a challenging task. Per-sonalized speech enhancement (PSE) endeavours to achievethis by…