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In this paper, we propose a new semi-parametric approach for blind source separation (BSS) of noisy mixtures with application to heavy-tailed signals. The semi-parametric statistical principle is used to formulate the BSS problem as a maximum likelihood (ML) estimation. More precisely, this approach consists of combining the logspline model for sources density approximation with a stochastic version...
In this paper we propose two iterative algorithms for the blind separation of convolutive mixtures of sparse signals. The first one, called Iterative Sparse Blind Separation (ISBS), minimizes a sparsity cost function using an approximate Newton technique. The second algorithm, referred to as Givens-based Sparse Blind Separation (GSBS) computes the separation matrix as a product of a whitening matrix...
In this letter, we elaborate a new version of the orthogonal projection approximation and subspace tracking (OPAST) for the extraction and tracking of the principal eigenvectors of a positive Hermitian covariance matrix. The proposed algorithm referred to as principal component OPAST (PC-OPAST) estimates the principal eigenvectors (not only a random basis of the principal subspace as for OPAST) of...
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