Overview of source separation applications

Publisher Summary This chapter provides an overview of history of source separation applications, presents how to apply independent component analysis, and describes a few (blind or semiblind) source separation applications. A “fully blind” approach to source separation would assume strictly no prior knowledge about either source properties or type of mixture. The source separation problem cannot be solved in such conditions. Therefore, the most classical source separation approaches, which are called “blind methods,” are based on generic priors. In order to successfully apply blind source separation (BSS) methods to practical problems, one first has to define the considered relationship between the observations and the sources, which is obtained by modeling the physics of the system when this is possible, and to check if the assumed source properties are realistic. When the number of free unknown parameters in independent component analysis (ICA) is too high, as compared to the available sample size, the ICA model is likely to overfit or overlearn the data. Solutions to the overfitting problem include, in addition to the acquisition of more data, a reduction of the dimensions of the data. Such a process can be performed during whitening, which typically precedes most ICA algorithms.

[1]  Christian Jutten,et al.  A geometric approach for separating post non-linear mixtures , 2002, 2002 11th European Signal Processing Conference.

[2]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[3]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[4]  Pierre Comon,et al.  Blind identification and source separation in 2×3 under-determined mixtures , 2004, IEEE Trans. Signal Process..

[5]  Laurent Albera,et al.  Second-order blind separation of first- and second-order cyclostationary sources-application to AM, FSK, CPFSK, and deterministic sources , 2004, IEEE Transactions on Signal Processing.

[6]  P. Deschamps,et al.  Atmospheric modeling for space measurements of ground reflectances, including bidirectional properties. , 1979, Applied optics.

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

[8]  Eric Moreau,et al.  Self-adaptive source separation. II. Comparison of the direct, feedback, and mixed linear network , 1998, IEEE Trans. Signal Process..

[9]  D. Sornette,et al.  Data-adaptive wavelets and multi-scale singular-spectrum analysis , 1998, chao-dyn/9810034.

[10]  E Bacharakis,et al.  Maternal and foetal ECG separation using blind source separation methods. , 1997, IMA journal of mathematics applied in medicine and biology.

[11]  Sylvain Douté,et al.  WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[12]  A. Mohammad-Djafari A Bayesian approach to source separation , 2000, math-ph/0008025.

[13]  Victoria Stodden,et al.  When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? , 2003, NIPS.

[14]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[15]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[16]  Philippe Loubaton,et al.  Separation of a class of convolutive mixtures: a contrast function approach , 2001, Signal Process..

[17]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[18]  Ralf Möller,et al.  A Self-Stabilizing Learning Rule for Minor Component Analysis , 2004, Int. J. Neural Syst..

[19]  P. Bak,et al.  Self-organized criticality. , 1988, Physical review. A, General physics.

[20]  Ricardo Vigário,et al.  Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions , 2003, J. Mach. Learn. Res..

[21]  Andreas Ziehe,et al.  TDSEP — an efficient algorithm for blind separation using time structure , 1998 .

[22]  Pierre Comon,et al.  Performance of HO Blind Source Separation Methods: Experimental Results on Ionospheric HF Links , 1999 .

[23]  Jon Atli Benediktsson,et al.  On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation , 2008, Neurocomputing.

[24]  Motoaki Kawanabe,et al.  A resampling approach to estimate the stability of one-dimensional or multidimensional independent components , 2002, IEEE Transactions on Biomedical Engineering.

[25]  José Millet-Roig,et al.  Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias , 2005, IEEE Transactions on Biomedical Engineering.

[26]  Jean-Marc Vesin,et al.  Observer of the autonomic cardiac outflow in humans using non-causal blind source separation , 1999 .

[27]  Asoke K. Nandi,et al.  Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation , 2001, IEEE Transactions on Biomedical Engineering.

[28]  S. T. Buckland,et al.  An Introduction to the Bootstrap , 1994 .

[29]  Eric Moreau,et al.  Self-adaptive source separation .I. Convergence analysis of a direct linear network controlled by the Herault-Jutten algorithm , 1997, IEEE Trans. Signal Process..

[30]  John Carlin,et al.  Bootstrapping adaptive cross pol cancelers for satellite communications , 1982 .

[31]  Erkki Oja,et al.  Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.

[32]  C. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[33]  P. Drossart,et al.  Perennial water ice identified in the south polar cap of Mars , 2004, Nature.

[34]  E. Oja,et al.  Independent Component Analysis , 2001 .

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

[36]  Patrik O. Hoyer,et al.  Non-negative sparse coding , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[37]  Athina P. Petropulu,et al.  Long-range dependence and heavy-tail modeling for teletraffic data , 2002, IEEE Signal Process. Mag..

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

[39]  B. Kowalski,et al.  Multivariate curve resolution applied to spectral data from multiple runs of an industrial process , 1993 .

[40]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[41]  J. Pekar,et al.  fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis , 2001, NeuroImage.

[42]  Aapo Hyvärinen,et al.  Complexity Pursuit: Separating Interesting Components from Time Series , 2001, Neural Computation.

[43]  Yannick Deville,et al.  Multi-tag radio-frequency identification systems based on new blind source separation neural networks , 2002, Neurocomputing.

[44]  T. Sejnowski,et al.  Independent component analysis of fMRI data: Examining the assumptions , 1998, Human brain mapping.

[45]  Jean-Marc Vesin,et al.  Observer of autonomic cardiac outflow based on blind source separation of ECG parameters , 2000, IEEE Transactions on Biomedical Engineering.

[46]  Joos Vandewalle,et al.  Fetal electrocardiogram extraction by blind source subspace separation , 2000, IEEE Transactions on Biomedical Engineering.

[47]  Shahram Hosseini,et al.  A multi-tag radio-frequency identification system using a new blind source separation method based on spectral decorrelation , 2006 .

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

[49]  David Brie,et al.  Non-negative source separation: range of admissible solutions and conditions for the uniqueness of the solution , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[50]  Christian Jutten,et al.  Identifiability of post-nonlinear mixtures , 2005, IEEE Signal Processing Letters.

[51]  Michael Breakspear,et al.  Spatiotemporal wavelet resampling for functional neuroimaging data , 2004, Human brain mapping.

[52]  Ehud Weinstein,et al.  Criteria for multichannel signal separation , 1994, IEEE Trans. Signal Process..

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

[54]  U. Scherrer,et al.  Observer of the human cardiac sympathetic nerve activity using noncausal blind source separation , 1999, IEEE Transactions on Biomedical Engineering.

[55]  T. Sejnowski,et al.  CONSISTENCY OF INFOMAX ICA DECOMPOSITION OF FUNCTIONAL BRAIN IMAGING DATA , 2003 .

[56]  Herbert Lee,et al.  Bagging and the Bayesian Bootstrap , 2001, AISTATS.

[57]  Juha Karhunen,et al.  Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures , 2004, Int. J. Neural Syst..

[58]  S. Douté,et al.  South Pole of Mars: Nature and composition of the icy terrains from Mars Express OMEGA observations , 2007 .

[59]  Klaus Obermayer,et al.  Blind signal separation from optical imaging recordings with extended spatial decorrelation , 2000, IEEE Transactions on Biomedical Engineering.

[60]  Jarkko Ylipaavalniemi,et al.  Analyzing consistency of independent components: An fMRI illustration , 2008, NeuroImage.

[61]  Mark D. Plumbley,et al.  Polyphonic music transcription by non-negative sparse coding of power spectra , 2004 .

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

[63]  Yuanqing Li,et al.  Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.

[64]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

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

[66]  Erkki Oja,et al.  Independent Component Analysis , 2001, IEEE Transactions on Neural Networks.

[67]  Jutten,et al.  1 - Une solution neuromimétique au problème de séparation de sources , 1988 .

[68]  R. Bro,et al.  A fast non‐negativity‐constrained least squares algorithm , 1997 .

[69]  C. Joblin,et al.  Analysis of the emission of very small dust particles from Spitzer spectro-imagery data using blind signal separation methods , 2007 .

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

[71]  Aapo Hyvärinen,et al.  Validating the independent components of neuroimaging time series via clustering and visualization , 2004, NeuroImage.

[72]  Erkki Oja,et al.  Independent component analysis for artefact separation in astrophysical images , 2003, Neural Networks.