Blind source separation has been intriguing many scientists. In adaptive signal processing, LMS (kast-mean squared) algorithm has long been used in signal enhancement and noise cancellation but it cannot ovexome the d$jiculty caused by the signal leakage into the reference input. Hence we have to explore more general statistical properties about the observed signals. This view corresponds to a statistical modeling of the signals using statistical measures such as a loss function, which is

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... ent from the mutual information. This paper will propose a new loss function based on generalized Gaussian distribution family and derive nav simple adaptive learning rules. Our separator based on the new generalized "anti-Hebbian rules" is also justified by the simulation on both artificial and real data with good pelfonnance.