  We study the problem of semi-supervised singing voice separation, in which
the training data contains a set of samples of mixed music (singing and
instrumental) and an unmatched set of instrumental music. Our solution employs
a single mapping function g, which, applied to a mixed sample, recovers the
underlying instrumental music, and, applied to an instrumental sample, returns
the same sample. The network g is trained using purely instrumental samples, as
well as on synthetic mixed samples that are created by mixing reconstructed
singing voices with random instrumental samples. Our results indicate that we
are on a par with or better than fully supervised methods, which are also
provided with training samples of unmixed singing voices, and are better than
other recent semi-supervised methods.
