The Research and Simulation of Blind Source Separation Algorithm
When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.
Year of publication: |
2016
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Authors: | Gao, Tao ; Li, Jincan |
Published in: |
International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC). - IGI Global, ISSN 1937-9668, ZDB-ID 2695914-8. - Vol. 8.2016, 3 (01.07.), p. 1-36
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Publisher: |
IGI Global |
Subject: | Blind Source Separation | EASI | FastICA | MATLAB Simulation | SOBI |
Saved in:
Online Resource
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