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首页> 外文期刊>Signal Processing: The Official Publication of the European Association for Signal Processing (EURASIP) >Self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: Stability analysis and optimization of asymptotic behaviour
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Self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: Stability analysis and optimization of asymptotic behaviour

机译：Self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: Stability analysis and optimization of asymptotic behaviour

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摘要

In this paper, we investigate the self-adaptive source separation problem for convolutively mixed signals. The proposed approach uses a recurrent structure adapted by a generic rule involving arbitrary separating functions. A stability analysis of this algorithm is first performed. It especially applies to some classical rules for instantaneous and convolutive mixtures that were proposed in the literature but only partly analyzed. The expression of the asymptotic error variance is then determined for strictly causal mixtures. This enables to derive the optimum separating functions that minimize this error variance. They are shown to be only related to the probability density functions of the sources. To perform this error minimization, two normalization procedures that improve the algorithm properties are proposed. Their stability conditions and their asymptotic behaviour are analyzed.

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