Jean-François Cardoso

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Separation of sources consists of 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 be “blindly” processed. This typically occurs in narrowband array processing applications when the array manifold is unknown or(More)
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)
Given an n × 1 random vector X, independent component analysis (ICA) consists of finding a basis of Rn on which the coefficients of X are as independent as possible (in some appropriate sense). The change of basis can be represented by an n × n matrix B and the new coefficients given by the entries of vector Y = BX. When the observation vector X is modeled(More)
This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimensional 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)
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)
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)
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 e ciencies of these algorithms are studied. The main results are as(More)