Robust Sparse Blind Source Separation

Blind Source Separation via Multinode Sparse Representation

We consider a problem of blind source separation from a set of instantaneous linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appropriate representation according to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multi scale transforms, such as wavelet or w...

Blind Source Separation via Multinode Sparse Representation

Underdetermined Sparse Blind Source Separation with Delays

In this paper, we address the problem of under-determined blind source separation (BSS), mainly for speech signals, in an anechoic environment. Our approach is based on exploiting the sparsity of Gabor expansions of speech signals. For parameter estimation, we adopt the clustering approach of DUET [19]. However, unlike in the case of DUET where only two mixtures are used, we use all available m...

Sparse coding blind source separation through Powerline

Without multiplexing, Powerline (PL) can support the concept of smart sensor web broadcasting of an N -sensors-to-single-owner (N -to-1) for household=stadium=mall=metro=city surveillance. In order to make the single user scenario feasible, the underdetermined blind source separation (BSS) problem x(k) = 〈a; s (k)〉+ n(k) has to be solved so that only inner product time series signal x(k) is kno...

Robust Blind Source Separation by Beta Divergence

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to ou...