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

Ofir Lindenbaum, Arie Yeredor, Ran Vitek, Moshe Mishali

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013 - Southampton, United Kingdom
Duration: 22 Sep 201325 Sep 2013

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013
Country/TerritoryUnited Kingdom
CitySouthampton
Period22/09/1325/09/13

Keywords

  • Blind Source Separation
  • Block-Sparsity
  • Dictionary Learning
  • Orthogonal Mixtures

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