One to multiple mapping dual learning: Learning multiple signals from one mixture

Abstract

Single channel blind source separation (SCBSS) refers to separating multiple sources from a mixture collected by a single sensor. Existing methods for SCBSS have limited performance in separating multiple sources and generalization. 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 achieve one-to-multiple cross-domain mapping. This algorithm can be applied to a variety of mixtures including both instantaneous and convolutive mixtures. In addition, new datasets for single channel source separation are created which include the mixtures and corresponding sources for this study. Experiments were performed on four different datasets including both one-dimensional and two-dimensional signals. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms, measured with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), source-to-distortion ratio (SDR), source-to-interferences ratio (SIR), relative root mean squared error (RRMSE) and correlation.