  Reverberation as supervision (RAS) is a framework that allows for training
monaural speech separation models from multi-channel mixtures in an
unsupervised manner. In RAS, models are trained so that sources predicted from
a mixture at an input channel can be mapped to reconstruct a mixture at a
target channel. However, stable unsupervised training has so far only been
achieved in over-determined source-channel conditions, leaving the key
determined case unsolved. This work proposes enhanced RAS (ERAS) for solving
this problem. Through qualitative analysis, we found that stable training can
be achieved by leveraging the loss term to alleviate the frequency-permutation
problem. Separation performance is also boosted by adding a novel loss term
where separated signals mapped back to their own input mixture are used as
pseudo-targets for the signals separated from other channels and mapped to the
same channel. Experimental results demonstrate high stability and performance
of ERAS.
