We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
@Turan111,
True it will improve results. However for a fair comparison with previous models in the literature we report results without dynamic mixing. Soon we will upgrade the code with more options (including dynamic mixing).
adiyoss
commented
3 years ago
Why don't you apply dynamic mixing in training? According to recent researches it gives better results.