  Single channel blind source separation (SCBSS) refers to separate multiple
sources from a mixed signal collected by a single sensor. The existing methods
for SCBSS mainly focus on separating two sources and have weak generalization
performance. To address these problems, an algorithm is proposed in this paper
to separate multiple sources from a mixture by designing a parallel dual
generative adversarial Network (PDualGAN) that can build the relationship
between a mixture and the corresponding multiple sources to realize
one-to-multiple cross-domain mapping. This algorithm can be applied to any
mixed model such as linear instantaneous mixed model and convolutional mixed
model. Besides, one-to-multiple datasets are created which including the
mixtures and corresponding sources for this study. The experiment was carried
out on four different datasets and tested with signals mixed in different
proportions. Experimental results show that the proposed algorithm can achieve
high performance in peak signal-to-noise ratio (PSNR) and correlation, which
outperforms state-of-the-art algorithms.
