Blind source separation and deconvolution by dynamic component analysis

被引：2
|
作者：
Attias, H
Schreiner, CE
机构：
来源：
NEURAL NETWORKS FOR SIGNAL PROCESSING VII | 1997年
关键词：
D O I：
10.1109/NNSP.1997.622427
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We derive new unsupervised learning rules for blind separation of mixed and convolved sources. These rules are nonlinear in the signals and thus exploit high-order spatiotemporal statistics to achieve separation. The derivation is based on a global optimization formulation of the separation problem, yielding a stable algorithm. Different rules are obtained from frequency-and time-domain optimization. We illustrate the performance of this method by successfully separating convolutive mixtures of speech signals.
引用
收藏
页码：456 / 465
页数：10
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