Title :
Blind Source Separation Algorithm Based on Maximum Signal Noise Ratio
Author :
Ma, Jiancang ; Zhang, Xiaobing
Abstract :
A low computational complexity instantaneous linear mixture signals blind separation algorithm was proposed, which is based on the character that Signal Noise Ratio (SNR) is maximal when statistically independent source signals are completely separated, and it is used as a separation contrast. Source signals are replaced by moving average of estimate signals. The function of covariance matrixes of the source signals and noises was expressed by the generalized eigenvalue (GE) problem, and unmixing matrix was achieved by solving the generalized eigenvalue problem without any iterative. Compared to the typical information-theoretical approaches, the merit of this algorithm is effectively and low complexity in computation.
Keywords :
blind source separation; computational complexity; covariance matrices; blind source separation algorithm; computational complexity; covariance matrices; estimate signal; generalized eigenvalue problem; instantaneous linear mixture signal; maximum signal noise ratio; separation contrast; source signal; unmixing matrix; Biomedical signal processing; Blind source separation; Computational complexity; Cost function; Covariance matrix; Eigenvalues and eigenfunctions; Intelligent networks; Signal processing algorithms; Signal to noise ratio; Source separation; blind source separation; generalized Eigenvalue; moving average; signal noise ratio;
Conference_Titel :
Intelligent Networks and Intelligent Systems, 2008. ICINIS '08. First International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3391-9
Electronic_ISBN :
978-0-7695-3391-9
DOI :
10.1109/ICINIS.2008.109
