A Computationally Efficient Blind Source Extraction Using Idempotent Transformation Matrix
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Blind source separation (BSS) problem is an open area of research that requires further investigations. Various algorithms were presented in the literature based on second-order statistics and higher-order statistics. The computational complexity of those methods is a challenging task and must be carefully considered to produce fast BSS algorithms. In blind source extraction (BSE) using linear predictors, the adaptive filter update requires complex computations that need consideration. This work focus on new BSE using the idempotent transformation matrix. New algorithm is presented in this work to compute the matrix with less computational complexity as compared with the standard singular value decomposition method. New optimization problem was defined according to the proposed matrix equation, and solved by an iterative algorithm with low computational complexity. The proposed method is tested using speech and white Gaussian signals. The performance measures used in this work are the signal-to-interference ratio, signal-to-distortion ratio, and signal-to-artifact ratio. Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with the state-of-the-art approaches such as second-order blind identification and fast independent component analysis.
KeywordsBlind source separation Blind source extraction Singular value decomposition Fast independent component analysis Second-order blind identification Speech signals.
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