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- Serge Dégerine, Abdelhamid Zaïdi
- IEEE Transactions on Signal Processing
- 2004

This paper deals with the problem of blind separation of an instantaneous mixture of Gaussian autoregressive sources, without additive noise, by the exact maximum likelihood approach. The maximization of the likelihood function is divided, using relaxation, into two suboptimization problems, solved by relaxation methods as well. The first one consists of… (More)

- Serge Dégerine, Elimane Kane
- IEEE Transactions on Signal Processing
- 2007

A comparative study of approximate joint diagonalization algorithms of a set of matrices is presented. Using a weighted least-squares criterion, without the orthogonality constraint, an algorithm is compared with an analogous one for blind source separation (BSS). The criterion of the present algorithm is on the separating matrix while the other is on the… (More)

- Serge Dégerine
- IEEE Trans. Signal Processing
- 1993

- Serge Dégerine, Rajaa Malki
- IEEE Trans. Signal Processing
- 2000

The second order properties of a process are usually characterized by the autocovariance function. In the stationary case, the parameterization by the partial autocorrelation function is relatively recent. We extend this parameterization to the nonstationary case. The advantage of this function is that it is subject to very simple constraints in comparison… (More)

- Serge Dégerine, Abdelhamid Zaïdi
- SIAM Journal on Optimization
- 2006

This paper presents the problem of maximizing the determinant of a K-square real matrix B, subject to the constraint that each row bk of B satisfies b t kΓkbk ≤ 1, where Γ1, . . . , ΓK , are K given real symmetric positive definite matrices. Existence and uniqueness of the solution is discussed. An iterative algorithm, using a method of relaxation type, is… (More)

- Dinh Tuan Pham, Serge Dégerine
- IEEE Trans. Acoustics, Speech, and Signal…
- 1990

- Thierry Chonavel, Serge Dégerine
- ICASSP
- 1996

- Thierry Chonavel, Serge Dégerine
- IEEE Trans. Information Theory
- 1999

A new algorithm for approximate joint diagonalization of a set of matrices is presented. Using a weighted leastsquares (WLS) criterion, without the orthogonality constraint, it is compared with an analoguous algorithm for blind source separation (BSS). The criterion of our algorithm is on the separating matrix while the other is on the mixing matrix. The… (More)