Abstract
Independent Component Analysis (ICA) is a computational method to solve Blind Source Separation (BSS) problem. In this study, an improved Fast ICA based on eighth-order Newton's method is proposed to solve BSS problems. Eight-order Newton's method for finding the solution of nonlinear equations is much faster than ordinary Newton's iterative method. The improved FastICA algorithm is applied to separate sound signals. The simulation results show the method has fewer iterations and faster convergence speed.
Highlights
Independent Component Analysis (ICA) or Blind Source Separation (BSS) is a signal processing method that extracts statistically independent components from a set of measured signals (Hyvarinen, 1998; Lee et al, 1999; Ye et al, 2006)
The FastICA algorithm was presented by Hyvarinen (1999)
A kind of Newton-type algorithm was used in FastICA and the algorithm was based on fixed-point algorithm iteration to maximize nongaussianity as a measure of statistical independence
Summary
Independent Component Analysis (ICA) or Blind Source Separation (BSS) is a signal processing method that extracts statistically independent components from a set of measured signals (Hyvarinen, 1998; Lee et al, 1999; Ye et al, 2006). In FastICA algorithm, the Newton iteration method with two-order convergence was employed in a fixedpoint algorithm for estimating the separation matrix. An improved FastICA based on an eighth-order convergence Newton’s Method (Li et al, 2011) is proposed. The update equations inside the FastICA algorithm is developed for estimating separation matrix with less iterative.
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