Blind extraction of temporally correlated but statistically dependent acoustic signals

Abstract. In this paper w e propose a batch learning algorithm for sequential blind extraction of arbitrary distributed but generally not i.i.d. (independent identically distributed) temporally corre-lated sources, possibly dependent speech signals from from linear mixture of them. The proposed algorithm is computationally very simple and efficient, it is based only on the second order statistics and in contrast to the most known algorithms developed for the sequential blind extraction and independent component analysis, do not assume statistical independence of source signals neither non-zero kurtosis for the sources, thus statistical dependent sig-nals including sources with extremely low or even zero kurtosis (colored Gaussian with different spectra) can be also successfully extracted. Extensive computer simulation confirm the validity and high performance of the proposed algorithm