Applications of neural blind separation to signal and image processing

In blind source separation one tries to separate statistically independent unknown source signals from their linear mixtures without knowing the mixing coefficients. Such techniques are currently studied actively both in statistical signal processing and unsupervised neural learning. We apply neural blind separation techniques developed in our laboratory to the extraction of features from natural images and to the separation of medical EEG signals. The new analysis method yields features that describe the underlying data better than for example classical principal component analysis. We discuss difficulties related with real-world applications of blind signal processing, too.

[1]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[2]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[3]  A. Hyvarinen.  A family of fixed-point algorithms for independent component analysis , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Erkki Oja,et al.  Image Feature Extraction Using Independent Component Analysis , 1996 .

[5]  Kari Torkkola,et al.  Blind separation of convolved sources based on information maximization , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

[6]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

[7]  Erkki Oja,et al.  One-unit Learning Rules for Independent Component Analysis , 1996, NIPS.

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

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

[10]  Ruey-Wen Liu,et al.  Blind signal processing: an introduction , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

[11]  Juha Karhunen,et al.  Nonlinear PCA type approaches for source separation and independent component analysis , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

[13]  Jean-Francois Cardoso,et al.  Adaptive Source Separation With Uniform Performance , 1994 .

[14]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[15]  T J Sejnowski,et al.  Learning the higher-order structure of a natural sound. , 1996, Network.

[16]  Juha Karhunen,et al.  Blind source separation using least-squares type adaptive algorithms , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Yves Deville,et al.  Application of blind source separation techniques to multi-tag contactless identification systems , 1995 .

[18]  Juha Karhunen,et al.  Neural approaches to independent component analysis and source separation , 1996, ESANN.

[19]  Lucas C. Parra,et al.  Statistical Independence and Novelty Detection with Information Preserving Nonlinear Maps , 1996, Neural Computation.

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

[21]  Pierre Comon,et al.  Independent component analysis, a survey of some algebraic methods , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.