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Workshop on Sparse Representations in Signal Processing, NIPS 2003

Signal processing methods relying on "sparse representations" have proven useful in compression, de-noising, classification, and inverse problems in imaging, acoustics/speech, and communications. Sparseness in the context of Blind Source Separation (BSS) or Independent Component Analysis (ICA) have led to powerful techniques capable of, for example, separating many speech signals from two (or in some cases just one!) mixtures. De-mixing more sources than sensors is of particular interest because the classical mathematical formulation of that problem is ill-posed, and only through the exploitation of sparse representations of the signals of interest have efficient solutions become possible. Sparse representations have also led to significant improvements in the performance of the BSS/ICA techniques in the standard case, when the number of sources is equal to or smaller than the number of sensors, as in the analysis of various sorts of functional brain imaging data.

 
 
New World article on Blind Source Separation, Dec 2001

Using an algorithm and two microphones, "blind source separation" can "de-mix" sounds and filter out individual voices from background noise. Developed by Siemens Corporate Research, the technology could play a key role in a wide range of personal, security and automotive applications.

 
 
Tutorial -- Inherent Characteristics and Biases of Evolution in genetic Programming, GECCO99  
 
Tutorial -- Characteristics and Biases of Evolution in genetic Programming, GP98  
 
Siemens' Researchers Untangle Wireless "Tower of Babel" to Improve Internet Data Transmission Rates, 2000  
 
Workshop on Genetic Programming -- From Theory to Real-World Applications, Machine Learning Conference, 1995
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