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 -