Heuristics For Efficient 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...

Sparse Kernel Independent Component Analysis for Blind Source Separation

We address the problem of Blind Source Separation (BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis (ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to est...