  We propose an independence-based joint dereverberation and separation method
with a neural source model. We introduce a neural network in the framework of
time-decorrelation iterative source steering, which is an extension of
independent vector analysis to joint dereverberation and separation. The
network is trained in an end-to-end manner with a permutation invariant loss on
the time-domain separation output signals. Our proposed method can be applied
in any situation with at least as many microphones as sources, regardless of
their number. In experiments, we demonstrate that our method results in high
performance in terms of both speech quality metrics and word error rate (WER),
even for mixtures with a different number of speakers than training.
Furthermore, the model, trained on synthetic mixtures, without any
modifications, greatly reduces the WER on the recorded dataset LibriCSS.
