5 papers
Is Phase Really Needed for Weakly-Supervised Dereverberation ?
Marius Rodrigues, Louis Bahrman, Roland Badeau +1
In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating…
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…
A Hybrid Model for Weakly-Supervised Speech Dereverberation
Louis Bahrman, Mathieu Fontaine, Gael Richard
This paper introduces a new training strategy to improve speech dereverberation systems using minimal acoustic information and reverberant (wet) speech. Most existing algorithms re…
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…
Signal Inpainting from Fourier Magnitudes
Louis Bahrman, Marina Krémé, Paul Magron +1
Signal inpainting is the task of restoring degraded or missing samples in a signal. In this paper we address signal inpainting when Fourier magnitudes are observed. We propose a ma…