Blind Source Separation by Sparse Decomposition in a Signal Dictionary

Blind Source Separation by Sparse Decomposition in a Signal Dictionary

The blind source separation problem is to extract the underlying source signals from a set of linear mixtures, where the mixing matrix is unknown. This situation is common in acoustics, radio, medical signal and image processing, hyperspectral imaging, and other areas. We suggest a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary (for instance, a wav...

Over-complete blind source separation by applying sparse decomposition and information theoretic based probabilistic approach

Both in the case of cellular communication and in the case of spoken dialogue based information retrieval systems on mobile platforms there exist a number of interference signals. Therefore, it is essential to separate these interference signals from the intended signal(s) in order to have clear communication in the case of cellular phone and to improve the speech recognition accuracy in the ca...

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...