In this paper we propose a batch learning algorithm for sequential blind extraction of arbitrary distributed but generally not i.i.d. (independent identically distributed) temporally correlated sources, possibly dependent speech signals 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 signals 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.
|Number of pages||10|
|Publication status||Published - 2000|
|Event||10th IEEE Workshop on Neural Netwoks for Signal Processing (NNSP2000) - Sydney, Australia|
Duration: 11 Dec 2000 → 13 Dec 2000
|Conference||10th IEEE Workshop on Neural Netwoks for Signal Processing (NNSP2000)|
|Period||11/12/00 → 13/12/00|