Flexible Nonlinear Learning for Source Separation

  • Park, Seung-Jin (the Department of Electrical Engineering, Chunbuk National University, 48 Jaeshin-dong, Cheonju, Chungbuk 361-763)
  • Published : 2000.09.01

Abstract

Source separation is a statistical method, the goal of which is to separate the linear instantaneous mixtures of statistically independent sources without resorting to any prior knowledge. This paper addresses a source separation algorithm which is able to separate the mixtures of sub- and super-Gaussian sources. The nonlinear function in the proposed algorithm is derived from the generalized Gaussian distribution that is a set of distributions parameterized by a real positive number (Gaussian exponent). Based on the relationship between the kurtosis and the Gaussian exponent, we present a simple and efficient way of selecting proper nonlinear functions for source separation. Useful behavior of the proposed method is demonstrated by computer simulations.

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