Before revision:  We show that Wavesplit outperforms the previous state-of-the-art on clean mixtures of 2 or 3 speakers ( WSJ0-2mix, WSJ0-3mix ), as well as in noisy (WHAM!) and reverberated (WHAMR!)conditions. As an additional contribution, we further improve our model by introducing online data augmentation for separation .
After revision:  We show that Wavesplit outperforms the previous state-of-the-art on clean mixtures of 2 or 3 speakers ( WSJ0-2mix, WSJ0-3mix ), as well as in noisy and reverberated settings (WHAM/WHAMR). We also set a new benchmark on the recent LibriMix dataset. Finally, we show that Wavesplit is also applicable to other domains, by separating fetal and maternal heart rates from a single abdominal electrocardiogram .