ADAPTIVE APPROACH FOR BLIND SOURCE SEPARATION OF NONLINEAR MIXING SIGNALS

Sayed Mohammed, Usama; Saber, Hany

doi:10.21608/jesaun.2009.126491

ADAPTIVE APPROACH FOR BLIND SOURCE SEPARATION OF NONLINEAR MIXING SIGNALS

Document Type : Research Paper

Authors

Usama Sayed Mohammed ¹
Hany Saber ²

¹ Electrical Engineering Department,Faculty of Engineering,Assiut University, Assiut, Egypt

² Department of Electrical Engineering, Faculty of Eng., South Valley University, Aswan, Egypt

10.21608/jesaun.2009.126491

Abstract

In this paper, a new technique to solve the nonlinear blind source separation problem (NBSS) is introduced. The method is based on the concept of reducing the high frequency component of the nonlinear mixed signal by dividing the mixed signal into blocks in the time domain, with any arbitrary size. To remove the distortion of the nonlinear function, the discreet cosine transform (DCT) is applied on each block. By adaptively adjusting the size of the DCT block of data, the highly correlated subblocks, can be estimated, then the correlation between the highly correlated sub-blocks can be reduced. To complete the separation process, the linear blind source separation (BSS) algorithm based on the wavelet transform is used to reduced the correlation between the highly correlated DCT subblock. Performed computer simulations have shown the effectiveness of the idea, even in presence of strong nonlinearities and synthetic mixture of real world data (like speech and image signals).

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