  We provide an example of a distribution preserving source separation method,
which aims at addressing perceptual shortcomings of state-of-the-art methods.
Our approach uses unconditioned generative models of signal sources.
Reconstruction is achieved by means of mix-consistent sampling from a
distribution conditioned on a realization of a mix. The separated signals
follow their respective source distributions, which provides an advantage when
separation results are evaluated in a listening test.

Audio and Speech Processing;Machine Learning;Sound