Abstract

The notion of equivariance is relevant to source separation because multiplication of mixed signals is equivalent to changing the unknown parameter (the mixing matrix) into another mixing matrix. Elaborating on this observation, a wide class of batch estimators of the mixing matrix is first shown to offer uniform performance: quality of separation does not depend on the hardness of the mixture. Equivariance is next extended to adaptive algorithms, by the device of `serial updating'. Adaptive separators based on such a learning rule also exhibit uniform performance in a strong sense. I Introduction: Source separation Source separation, blind array processing, signal copy, independent component analysis, waveform preserving estimation: : : : these keywords refer to a signal model which is receiving increasing attention in both signal processing and neural network literature since the seminal paper [1]. This model is that of n statistically independent signals whose m (possibly noisy) l...

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