IDR Logo

Please use this identifier to cite or link to this item:
Title: Single channel blind source separation based on variational mode decomposition and PCA
Authors: Dey P.
Satija U.
Ramkumar B.
Keywords: Blind source separation (BSS)
principal component analysis (PCA)
variational mode decomposition (VMD)
Issue Date: 2016
Citation: 4
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. � 2015 IEEE.
Appears in Collections:Research Publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.