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