Single channel blind source separation based on variational mode decomposition and PCA

Abstract

Blind source separation plays an important role in extracting the source components from one or more mixture(s) of the sources received by a sensor or receiver. It is blind since no other information besides the observed mixture signals is available. In presence of only one observed mixture, it is known as single channel blind source separation (SCBSS). This paper proposes a method of SCBSS based on variational mode decomposition (VMD) and principal component analysis (PCA). The observed signal is decomposed into a number of modes simultaneously using VMD. Then, PCA is used to select the corresponding source components from the decomposed modes. An example illustrating the separation of a sinusoid and speech signal by our proposed method has been presented. Cross-correlation coefficient has been used to measure the performance of the VMD-PCA algorithm. Our method has been compared with the single channel independent component analysis (SCICA) and ensemble empirical mode decomposition-principal component analysis-independent component analysis (EEMD-PCA-ICA) methods in terms of quality of performance and computational complexity.