  We introduce two unsupervised source separation methods, which involve
self-supervised training from single-channel two-source speech mixtures. Our
first method, mixture permutation invariant training (MixPIT), enables learning
a neural network model which separates the underlying sources via a challenging
proxy task without supervision from the reference sources. Our second method,
cyclic mixture permutation invariant training (MixCycle), uses MixPIT as a
building block in a cyclic fashion for continuous learning. MixCycle gradually
converts the problem from separating mixtures of mixtures into separating
single mixtures. We compare our methods to common supervised and unsupervised
baselines: permutation invariant training with dynamic mixing (PIT-DM) and
mixture invariant training (MixIT). We show that MixCycle outperforms MixIT and
reaches a performance level very close to the supervised baseline (PIT-DM)
while circumventing the over-separation issue of MixIT. Also, we propose a
self-evaluation technique inspired by MixCycle that estimates model performance
without utilizing any reference sources. We show that it yields results
consistent with an evaluation on reference sources (LibriMix) and also with an
informal listening test conducted on a real-life mixtures dataset (REAL-M).
