One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal
This addresses a limitation in single-channel blind source separation for applications like audio or sensor data, though it appears incremental as it extends existing dual learning approaches to multiple sources.
The paper tackles the problem of separating multiple sources from a single-channel mixed signal, proposing a PDualGAN algorithm that achieves high performance in PSNR and correlation, outperforming state-of-the-art methods on four datasets.
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.