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CSDA
2007
169views more  CSDA 2007»
13 years 7 months ago
A null space method for over-complete blind source separation
In blind source separation, there are M sources that produce sounds independently and continuously over time. These sounds are then recorded by m receivers. The sound recorded by ...
Ray-Bing Chen, Ying Nian Wu
ICA
2010
Springer
13 years 5 months ago
Second Order Subspace Analysis and Simple Decompositions
Abstract. The recovery of the mixture of an N-dimensional signal generated by N independent processes is a well studied problem (see e.g. [1,10]) and robust algorithms that solve t...
Harold W. Gutch, Takanori Maehara, Fabian J. Theis
PAMI
2010
168views more  PAMI 2010»
13 years 5 months ago
Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization
—Although significant efforts have been made in developing nonnegative blind source separation techniques, accurate separation of positive yet dependent sources remains a challen...
Fa-Yu Wang, Chong-Yung Chi, Tsung-Han Chan, Yue Wa...
TASLP
2010
138views more  TASLP 2010»
13 years 2 months ago
Glimpsing IVA: A Framework for Overcomplete/Complete/Undercomplete Convolutive Source Separation
Abstract--Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that significantly reduces the occurrence of the well-known permutation problem in...
Alireza Masnadi-Shirazi, Wenyi Zhang, Bhaskar D. R...
ESANN
2000
13 years 8 months ago
Parametric approach to blind deconvolution of nonlinear channels
A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that ...
Jordi Solé i Casals, Anisse Taleb, Christia...