Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
The Journal of Machine Learning Research
Joint anti-diagonalization for blind source separation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A generalization of joint-diagonalization criteria for sourceseparation
IEEE Transactions on Signal Processing
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This paper addresses the problem of the non-unitary approximate joint block diagonalization (NU - JBD) of matrices. Such a problem occurs in various fields of applications among which blind separation of convolutive mixtures of sources and wide-band signals array processing. We present a new algorithm for the non-unitary joint block-diagonalization of complex matrices based on a gradient-descent algorithm whereby the optimal step size is computed algebraically at each iteration as the rooting of a 3rd-degree polynomial. Computer simulations are provided in order to illustrate the effectiveness of the proposed algorithm.