183 citations · 234 across the 3 of their papers we have counts for
4 papers
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…
Filterbank design for end-to-end speech separation
Manuel Pariente, Samuele Cornell, Antoine Deleforge +1
Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for…
A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders
Manuel Pariente, Antoine Deleforge, Emmanuel Vincent
Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speec…