Blind separation of sources, Part II: problems statement
Signal Processing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A Geometrical Based Procedure for Source Separation Mapped to a Neural Network
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
A direct solution for blind separation of sources
IEEE Transactions on Signal Processing
Second-order blind separation of sources based on canonical partialinnovations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Fourth-order criteria for blind sources separation
IEEE Transactions on Signal Processing
A subspace algorithm for certain blind identification problems
IEEE Transactions on Information Theory
New Method for Filtered ICA Signals Applied To Volatile Time Series.
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
An edge-finding algorithm on blind source separation for digital wireless applications
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Instantaneous MISO separation of BPSK sources
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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In this paper, we propose a new and simple algorithm for blind separation of sources based on geometrical concepts. This algorithm deals with the instantaneous mixtures and does not require the estimation of high-order statistics (HOS). The proposed algorithm can separate sources that belong to two different categories: (1) Signals with uniform or close to uniform probability density function (PDF). (2) Unimodal PDF (including Pascal and Gamma) as well as some real signals such as speech or music. Unfortunately, the actual version of the algorithm cannot deal with the mixing of signals from both categories. Finally, the experimental results show good performances and that the separation can be carried out in a very short time (a few seconds, using Matematica as the programming language and our ultra 30 Sun station).