514 citations · 809 across the 45 of their papers we have counts for
3 papers · 1 filter
Attention is All You Need in Speech Separation
Cem Subakan, Mirco Ravanelli, Samuele Cornell +2
Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parall…
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…