On Blind Methods in Signal Processing

Blind methods are powerful tools when very weak information is necessary. Although many algorithms can be called blind, in this paper, we focus on blind source separation (BSS) and independent component analysis (ICA). After a discussion concerning the blind nature of these techniques, we review three main points: the separability, the criteria, the algorithms.

[1]  Christian Jutten,et al.  Separating Convolutive Mixtures by Mutual Information Minimization , 2001, IWANN.

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

[3]  Visa Koivunen,et al.  Blind identifiability of class of nonlinear instantaneous ICA models , 2002, 2002 11th European Signal Processing Conference.

[4]  Christian Jutten,et al.  Source separation in post-nonlinear mixtures , 1999, IEEE Trans. Signal Process..

[5]  R. Liu,et al.  AMUSE: a new blind identification algorithm , 1990, IEEE International Symposium on Circuits and Systems.

[6]  Andreas Ziehe,et al.  TDSEP { an e(cid:14)cient algorithm for blind separation using time structure , 1998 .

[7]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[8]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[9]  Christian Jutten,et al.  Quasi-nonparametric blind inversion of Wiener systems , 2001, IEEE Trans. Signal Process..

[10]  Kiyotoshi Matsuoka,et al.  A neural net for blind separation of nonstationary signals , 1995, Neural Networks.

[11]  Christian Jutten,et al.  Detection de grandeurs primitives dans un message composite par une architecture de calcul neuromime , 1985 .

[12]  D. Donoho.  NATURE VS . MATH : INTERPRETING INDEPENDENT COMPONENT ANALYSIS IN LIGHT OF COMPUTATIONAL HARMONIC ANALYSIS , 2000 .

[13]  Radivoje Popovic,et al.  Hall effect devices , 1991 .

[14]  R W Prager,et al.  Development of low entropy coding in a recurrent network. , 1996, Network.

[15]  Adrian M. Ionescu,et al.  High Performance Magnetic Field Smart Sensor Arrays with Source Separation , 1998 .

[16]  Christian Jutten,et al.  BLIND SEPARATING CONVOLUTIVE POST NON-LINEAR MIXTURES , 2001 .

[17]  Calyampudi R. Rao,et al.  Extension of Darmois-Skitovic Theorem to Functions of Random Variables Satisfying an Addition Theorem , 1973 .

[18]  Pierre Comon,et al.  Separation Of Sources Using Higher-Order Cumulants , 1989, Optics & Photonics.

[19]  Dimitrios Hatzinakos,et al.  Blind identification of LTI-ZMNL-LTI nonlinear channel models , 1995, IEEE Trans. Signal Process..

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

[21]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[22]  Christian Jutten,et al.  BLIND SEPARATION OF TEMPORALLY CORRELATED SOURCES USING A QUASI MAXIMUM LIKELIHOOD APPROACH , 2001 .

[23]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

[24]  T. Ens,et al.  Blind signal separation : statistical principles , 1998 .

[25]  J. Aczél,et al.  Lectures on Functional Equations and Their Applications , 1968 .

[26]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[27]  Yannick Deville,et al.  Optimization of the asymptotic performance of time-domain convolutive source separation algorithms , 1997, ESANN.

[28]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[29]  Pierre Comon.  Independent component analysis - a new concept? signal processing , 1994 .

[30]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

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

[32]  M. J. Korenberg,et al.  The identification of nonlinear biological systems: LNL cascade models , 1986, Biological Cybernetics.

[33]  Serge Dégerine,et al.  Second-order blind separation of sources based on canonical partial innovations , 2000, IEEE Trans. Signal Process..

[34]  Andrzej Cichocki,et al.  Robust learning algorithm for blind separation of signals , 1994 .

[35]  M. Wax,et al.  A least-squares approach to joint diagonalization , 1997, IEEE Signal Processing Letters.

[36]  P. Comon,et al.  Contrasts for multichannel blind deconvolution , 1996, IEEE Signal Processing Letters.

[37]  D. Ruderman,et al.  INDEPENDENT COMPONENT ANALYSIS OF NATURAL IMAGE SEQUENCES YIELDS SPATIOTEMPORAL FILTERS SIMILAR TO SIMPLE CELLS IN PRIMARY VISUAL CORTEX , 1998 .

[38]  W. J. Hall,et al.  ON CHARACTERIZATION OF THE GAMMA DISTRIBUTION. , 1968 .

[39]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[40]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[41]  Pierre Comon,et al.  Séparation de mélanges de signaux , 1989 .

[42]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..