Random Pruning of Blockwise Stationary Mixtures for Online BSS
We explore information redundancy of linearly mixed sources in order to accomplish the demixing task (BSS) by ICA techniques in real-time. Assuming piecewise stationarity of the sources, the idea is to prune uniformly and independently most of sample data while preserving the ability of Kurtosis-based algorithms to reconstruct the original sources using pruned mixtures instead of original ones. The mainstay of this method is to control the sub-mixtures size so that the Kurtosis is sharply concentrated about that of the entire mixtures with exponentially small error probabilities. Referring to the FastICA algorithm, it is shown that the dimensionality reduction proposed while assuring high quality of the source estimate yields to a significant reduction of the demixing time. In particular, it is experimentally shown that, in case of online applications, the pruning of blockwise stationary data is not only essential for guarantying the time-constraints keeping, but it is also effective.
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