Independent component analysis based on sliding window

被引：2

作者：

Wu, Xiaopei ^{[1
,2
]}

Ye, Zhongfu ^{[2
]}

Guo, Xiaojing ^{[1
]}

Zhang, Daoxin ^{[1
]}

Hu, Renjun ^{[1
]}

机构：

[1] Key Laboratory of Intelligent Computing and Signal Processing, Anhui University

[2] Institute of Statistical Signal Processing, University of Science and Technology of China

来源：

Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2007年 / 44卷 / 01期

关键词：

Blind source separation; Independent component analysis; Sliding window;

D O I：

10.1360/crad20070126

中图分类号：

学科分类号：

摘要：

Independent component analysis (ICA) has become a hotspot in signal processing area, and its computation algorithms and application are widely studied recently. In this paper, a sliding window ICA algorithm is studied to deal with time variant mixing model that the traditional ICA algorithms fail to work. For solving the problem caused by the indeterminacy of ICs in applying sliding window ICA algorithm, the value of kurtosis is employed as the index to sort the independent components at each window position. This idea is proved to be effective in most cases. In addition, a recursive learning rule of separation matrix based on sliding window is given, which can reduce the computation load of the algorithm obviously. The selection of window length is also discussed in this paper. Furthermore, the separation performance of the proposed algorithm are compared with the batch ICA algorithm. Experiment results show that the proposed algorithm can work well in the time variant mixing model and can be used for online blind source separation and dynamic independent component analysis.

引用

页码：185 / 191

页数：6

共 8 条

[1]

Bell A.J., An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7, 4, pp. 1129-1159, (1995)

[2]

Hyvarinen A., Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans on Neural Networks, 10, 3, pp. 626-634, (1999)

[3]

Lee T.-W., Independent Component Analysis-Theory and Application, pp. 27-64, (1998)

[4]

Delfosse N., Adaptive blind separation of independent sources: A deflation approach, Signal Processing, 45, 1, pp. 59-83, (1995)

[5]

Amari S.I., Supereffeciency in blind source separation, IEEE Trans on Signal Processing, 47, 3, pp. 936-944, (1999)

[6]

Mandic D., Cichocki A., An online algorithm for blind extraction of sources with different dynamical structures, (2003)

[7]

Yeredor A., An expansion of SOBI for linearly time-varying mixtures, (2003)

[8]

Wu X., Ye Z., Online infomax algorithm and its application to artifact removal in long EEG recordings, Journal of Circuits and Systems, 10, 5, pp. 83-88, (2005)

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