  Separating two sources from an audio mixture is an important task with many
applications. It is a challenging problem since only one signal channel is
available for analysis. In this paper, we propose a novel framework for singing
voice separation using the generative adversarial network (GAN) with a
time-frequency masking function. The mixture spectra is considered to be a
distribution and is mapped to the clean spectra which is also considered a
distribtution. The approximation of distributions between mixture spectra and
clean spectra is performed during the adversarial training process. In contrast
with current deep learning approaches for source separation, the parameters of
the proposed framework are first initialized in a supervised setting and then
optimized by the training procedure of GAN in an unsupervised setting.
Experimental results on three datasets (MIR-1K, iKala and DSD100) show that
performance can be improved by the proposed framework consisting of
conventional networks.
