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- Adel Belouchrani, Karim Abed-Meraim, Jean-François Cardoso, Eric Moulines
- IEEE Trans. Signal Processing
- 1997

| Separation of sources consists in recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: the linear mixture should bèblindly' processed. This typically occurs in narrow-band array processing applications when the array manifold is unknown or… (More)

- Jean-François Cardoso, Beate H. Laheld
- IEEE Trans. Signal Processing
- 1996

| Source separation consists in recovering a set of independent signals when only mixtures with unknown coeecients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Equivariant Adaptive Separation via Independence). The EASI… (More)

This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.

| Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or`sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the… (More)

- Eric Moulines, Pierre Duhamel, Jean-François Cardoso, Sylvie Mayrargue
- ICASSP
- 1994

- Jean-François Cardoso
- ICASSP
- 1998

This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimen-sional Independent Component Analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly parameterized in terms of one-dimensional subspaces. From… (More)

- Jean-François Cardoso, Antoine Souloumiac
- SIAM J. Matrix Analysis Applications
- 1996

- Dinh-Tuan Pham, Jean-François Cardoso
- IEEE Trans. Signal Processing
- 2001

Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible) non-stationarity of the sources to achieve separation. We introduce two objective functions based on the likelihood and on mutual information in a simple Gaussian non stationary model and we show how they can be optimized, off-line or on-line, by… (More)

- Shun-ichi Amari, Jean-François Cardoso
- IEEE Trans. Signal Processing
- 1997

The semiparametric statistical model is used to formulate the problem of blind source separation. The method of estimating functions is applied to this problem. It is shown that estimation of the mixing matrix or its learning rule version is given by an estimating function. The statistical eciencies of these algorithms are studied. The main results are as… (More)

This communication presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higher-order moments, to identify source signatures in array data without any a-priori model for propagation or reception, that is,… (More)