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Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals

Jafari, MG, Wang, W, Chambers, JA, Hoya, T, Cichocki, A and Cichocki, A (2006) Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals IEEE Transactions on Signal Processing, 54 (3). pp. 1028-1040.

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Abstract

A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method. © 2006 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Jafari, MGUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Chambers, JAUNSPECIFIEDUNSPECIFIED
Hoya, TUNSPECIFIEDUNSPECIFIED
Cichocki, AUNSPECIFIEDUNSPECIFIED
Cichocki, AUNSPECIFIEDUNSPECIFIED
Date : March 2006
Identification Number : 10.1109/TSP.2005.863005
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37
URI: http://epubs.surrey.ac.uk/id/eprint/596093

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