Fast constrained independent component analysis for blind speech separation with multiple references

In previous work, the constrained independent component analysis (cICA) algorithm has been proposed to extract the interested signals from the mixtures of some source signals. However, the simultaneous extraction of all signals at the same time presented by cICA prolongs the processing time of this algorithm to extract output signals. In this paper, we introduce a new version of the cICA algorithm to improve cICA in the computational time aspect. By whitening input signals, normalizing weight vectors, and using the one-by-one extraction of output signals, our proposed cICA algorithm has reduced the computational time to recover original signals when compared with the conventional cICA. Meanwhile our proposed cICA algorithm still retains the same recovering performance with that of the conventional cICA. Moreover, in this paper, we also introduce a potential application of our proposed cICA and the conventional cICA on the speech separation problem using priori information to extract the interested speech signals from mixed signals.