Higher Order Moments Algorithms for Blind Signal Separation
نویسندگان
چکیده
An on-line learning algorithm, which minimizes a criterion based on geometrical properties, is derived for blind separation of mixed signals. This new contrast function focuses on the concept of center of masses and higher order moments (HOM) applied to the outputs. The source signals and the mixing matrix are unknown except for the number of sources. A set of estimating equations is obtained. The relative (natural) gradient is used as learning law. This new algorithm is related to the Maximum Likelihood approaches providing a new point of view for understanding them. It is concluded that HOM methods outperform them. Some results are included for audio and synthetic signals. These results show how the algorithm proposed presents better convergence in comparison to other well-known approaches.
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