Fast and accurate methods of independent component analysis: A Survey

This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG).

[1]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[2]  Dinh-Tuan Pham,et al.  Blind separation of instantaneous mixtures of nonstationary sources , 2001, IEEE Trans. Signal Process..

[3]  Dinh-Tuan Pham,et al.  Independent Component Analysis: Separation of non-stationary sources: algorithms and performance , 2001 .

[4]  Serge Dégerine,et al.  Separation of an instantaneous mixture of Gaussian autoregressive sources by the exact maximum likelihood approach , 2004, IEEE Transactions on Signal Processing.

[5]  Sergio Cruces,et al.  Thin QR and SVD factorizations for simultaneous blind signal extraction , 2004, 2004 12th European Signal Processing Conference.

[6]  E. Oja,et al.  Performance Analysis of the FastICA Algorithm and Cramér – Rao Bounds for Linear Independent Component Analysis , 2010 .

[7]  Erkki Oja,et al.  Performance analysis of the FastICA algorithm and Crame/spl acute/r-rao bounds for linear independent component analysis , 2006, IEEE Transactions on Signal Processing.

[8]  P. Tichavský,et al.  Efficient variant of algorithm fastica for independent component analysis attaining the cramer-RAO lower bound , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[9]  Arie Yeredor,et al.  A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach , 2008, IEEE Transactions on Neural Networks.

[10]  Zbynek Koldovsky,et al.  Time-Domain Blind Separation of Audio Sources on the Basis of a Complete ICA Decomposition of an Observation Space , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[11]  Erkki Oja,et al.  Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining the CramÉr-Rao Lower Bound , 2006, IEEE Transactions on Neural Networks.

[12]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[13]  Vwani P. Roychowdhury,et al.  Independent component analysis based on nonparametric density estimation , 2004, IEEE Transactions on Neural Networks.

[14]  John W. Fisher,et al.  ICA Using Spacings Estimates of Entropy , 2003, J. Mach. Learn. Res..

[15]  P. Tichavsky,et al.  Fast Approximate Joint Diagonalization Incorporating Weight Matrices , 2009, IEEE Transactions on Signal Processing.

[16]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[17]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[18]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[19]  Dinh Tuan Pham,et al.  Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion , 2000, 2000 10th European Signal Processing Conference.

[20]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[21]  Erkki Oja,et al.  Speed and Accuracy Enhancement of Linear ICA Techniques Using Rational Nonlinear Functions , 2007, ICA.

[22]  Christopher J James,et al.  Independent component analysis for biomedical signals , 2005, Physiological measurement.

[23]  Erkki Oja,et al.  Corrections to “Performance Analysis of the FastICA Algorithm and CramÉr–Rao Bounds for Linear Independent Component Analysis” , 2008, IEEE Transactions on Signal Processing.

[24]  Terrence J. Sejnowski,et al.  Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis , 2007, NeuroImage.

[25]  Dinh Tuan Pham,et al.  Joint Approximate Diagonalization of Positive Definite Hermitian Matrices , 2000, SIAM J. Matrix Anal. Appl..

[26]  Beat Kleiner,et al.  Graphical Methods for Data Analysis , 1983 .

[27]  Zbynek Koldovský,et al.  Comparison of independent component and independent subspace analysis algorithms , 2009, 2009 17th European Signal Processing Conference.

[28]  Yannick Deville,et al.  Blind separation of piecewise stationary non-Gaussian sources , 2009, Signal Process..