Adaptive weighted orthogonal constrained algorithm for blind source separation

Authors:
Jimin Ye;Haihong Jin;Qingrui Zhang
Affiliations:
Mathematics Department, School of Science, Xidian University, Xian 710071, China;School of Science, Xian Shiyou University, Xian 710065, China;Mathematics Department, School of Science, Xidian University, Xian 710071, China
Venue:
Digital Signal Processing
Year:
2013

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Abstract

Blind source separation (BSS) consists of recovering the statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Pre-whitening is a useful pre-processing technique in BSS. However, BSS algorithms based on the pre-whitened data lack the equivariance property, one of the significant properties in BSS. By transforming the pre-whitening into a weighted orthogonal constraint condition, this paper proposes a new definition of the contrast function. In light of the constrained optimization method, various weighted orthogonal constrained BSS algorithms with equivariance property are developed. Simulations on man-made signals and practical speech signals show the proposed weighted orthogonal constrained BSS algorithms have better separation ability, convergent speed and steady state performance.