TY - GEN

T1 - Blind separation of spatially-block-sparse sources from orthogonal mixtures

AU - Lindenbaum, Ofir

AU - Yeredor, Arie

AU - Vitek, Ran

AU - Mishali, Moshe

PY - 2013

Y1 - 2013

N2 - We addresses the classical problem of blind separation of a static linear mixture, where separation is not based on statistical assumptions (such as independence) regarding the sources, but rather on their spatial (block-) sparsity, and with an additional constraint of an orthogonal mixing-matrix. An algorithm for this problem was recently proposed by Mishali and Eldar, and consists of two steps: one for recovering the support of the sources, and a subsequent one for recovering their values. That algorithm has two shortcomings: One is an assumption that the spatial sparsity level of the sources at each time-instant is constant and known; The second is the algorithm's sensitivity to the possible presence of temporal 'blocks' of the signals with identical support. In this work we propose two pre-processing stages for improving the applicability and the performance of the algorithm. A first stage is aimed at identifying 'blocks' of similar support, and pruning the data accordingly for the support-recovery stage. A second stage is aimed at recovering the sparsity level at each time-instant by exploiting observed structural inter-relations between the signals at different time-instants. We demonstrate the improvement over the original algorithm using both synthetic data and mixed text-images. We also show that the algorithm outperforms the recovery rate of alternative source separation methods for such contexts, including K-SVD, a leading method for dictionary learning.

AB - We addresses the classical problem of blind separation of a static linear mixture, where separation is not based on statistical assumptions (such as independence) regarding the sources, but rather on their spatial (block-) sparsity, and with an additional constraint of an orthogonal mixing-matrix. An algorithm for this problem was recently proposed by Mishali and Eldar, and consists of two steps: one for recovering the support of the sources, and a subsequent one for recovering their values. That algorithm has two shortcomings: One is an assumption that the spatial sparsity level of the sources at each time-instant is constant and known; The second is the algorithm's sensitivity to the possible presence of temporal 'blocks' of the signals with identical support. In this work we propose two pre-processing stages for improving the applicability and the performance of the algorithm. A first stage is aimed at identifying 'blocks' of similar support, and pruning the data accordingly for the support-recovery stage. A second stage is aimed at recovering the sparsity level at each time-instant by exploiting observed structural inter-relations between the signals at different time-instants. We demonstrate the improvement over the original algorithm using both synthetic data and mixed text-images. We also show that the algorithm outperforms the recovery rate of alternative source separation methods for such contexts, including K-SVD, a leading method for dictionary learning.

KW - Blind Source Separation

KW - Block-Sparsity

KW - Dictionary Learning

KW - Orthogonal Mixtures

UR - http://www.scopus.com/inward/record.url?scp=84893232314&partnerID=8YFLogxK

U2 - 10.1109/mlsp.2013.6661896

DO - 10.1109/mlsp.2013.6661896

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AN - SCOPUS:84893232314

SN - 9781479911806

T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

BT - 2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013

T2 - 2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013

Y2 - 22 September 2013 through 25 September 2013

ER -