183 citations · 272 across the 7 of their papers we have counts for
8 papers
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge
Simon Leglaive, Matthieu Fraticelli, Hend ElGhazaly +7
Supervised models for speech enhancement are trained using artificially generated mixtures of clean speech and noise signals. However, the synthetic training conditions may not acc…
The CHiME-7 UDASE task: Unsupervised domain adaptation for conversational speech enhancement
Simon Leglaive, Léonie Borne, Efthymios Tzinis +6
Supervised speech enhancement models are trained using artificially generated mixtures of clean speech and noise signals, which may not match real-world recording conditions at tes…
Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge
Samuele Cornell, Zhong-Qiu Wang, Yoshiki Masuyama +3
This paper describes our submission to the Second Clarity Enhancement Challenge (CEC2), which consists of target speech enhancement for hearing-aid (HA) devices in noisy-reverberan…
Learning Filterbanks for End-to-End Acoustic Beamforming
Samuele Cornell, Manuel Pariente, François Grondin +1
Recent work on monaural source separation has shown that performance can be increased by using fully learned filterbanks with short windows. On the other hand it is widely known th…
LibriMix: An Open-Source Dataset for Generalizable Speech Separation
Joris Cosentino, Manuel Pariente, Samuele Cornell +2
In recent years, wsj0-2mix has become the reference dataset for single-channel speech separation. Most deep learning-based speech separation models today are benchmarked on it. How…
Asteroid: the PyTorch-based audio source separation toolkit for researchers
Manuel Pariente, Samuele Cornell, Joris Cosentino +11
This paper describes Asteroid, the PyTorch-based audio source separation toolkit for researchers. Inspired by the most successful neural source separation systems, it provides all…