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Single channel blind source separation (SCBSS) refers to separate multiplesources from a mixed signal collected by a single sensor. The existing methodsfor SCBSS mainly focus on separating two sources and have weak generalizationperformance. To address these problems, an algorithm is proposed in this paperto separate multiple sources from a mixture by designing a parallel dualgenerative adversarial Network (PDualGAN) that can build the relationshipbetween a mixture and the corresponding multiple sources to realizeone-to-multiple cross-domain mapping. This algorithm can be applied to anymixed model such as linear instantaneous mixed model and convolutional mixedmodel. Besides, one-to-multiple datasets are created which including themixtures and corresponding sources for this study. The experiment was carriedout on four different datasets and tested with signals mixed in differentproportions. Experimental results show that the proposed algorithm can achievehigh performance in peak signal-to-noise ratio (PSNR) and correlation, whichoutperforms state-of-the-art algorithms.

One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal