Computer Science ›› 2014, Vol. 41 ›› Issue (12): 78-81.doi: 10.11896/j.issn.1002-137X.2014.12.017

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Novel Algorithm of Blind Source Separation with Temporal Structure Based on Givens Transformation Matrix

ZHAO Li-xiang and LIU Guo-qing   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Independent Component Analysis (ICA) is an efficient method to solve the Blind Source Separation (BSS) problem with temporal structure.The key to ICA for whiten observation signals is to find an orthogonal matrix to throw away high-order redundancy between components.Given this problem,we proposed the parametric representation of orthogonal matrix in arbitrary dimension using Givens transformation matrix.Based on this,a new separation algorithm was proposed.Firstly,we decreased the number of parameters to be estimated by parameterizing the orthogonal matrix using Givens transformation matrix.Secondly,we converted the BSS problem into an unconstrained optimization problem,where the object function is the joint approximate diagonalization of multistep delayed covariance matrices.In order to estimate the parameters in orthogonal matrix,BFGS algorithm of quasi-Newton method was provided solving the unconstrained optimization problem.Finally,the separation for real mixed voice signals shows the effectiveness of our algorithm.

Key words: Blind source separation,Temporal structure,Independent component analysis,Orthogonal matrix,Givens transformation matrix

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