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| 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)
| 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)
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)
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)