Blind source separation by nonstationarity of variance:: A cumulant-based approach

被引:81
作者
Hyvärinen, A
机构
[1] Aalto Univ, Neural Networks Res Ctr, FIN-02015 Helsinki, Finland
[2] Univ Helsinki, Gen Psychol Div, Dept Psychol, FIN-00014 Helsinki, Finland
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2001年 / 12卷 / 06期
关键词
blind source separation; cumulants; independent component analysis; nonstationarity; statistical signal processing;
D O I
10.1109/72.963782
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Blind separation of source signals usually relies either on the nongaussianity of the signals or on their linear autocorrelations. A third approach was introduced by Matsuoka et al., who showed that source separation can be performed by using the nonstationarity of the signals, in particular the nonstationarity of their variances. In this paper, we show how to interpret the nonstationarity due to a smoothly changing variance in terms of higher order cross-cumulants. This is based on considering the time-correlation of the squares (energies) of the signals and leads to a simple optimization criterion. Using this criterion, we construct a fixed-point algorithm that is computationally very efficient.
引用
收藏
页码:1471 / 1474
页数:4
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