Sparsity and Adaptivity for the Blind Separation of Partially Correlated Sources

@article{Bobin2015SparsityAA,
  title={Sparsity and Adaptivity for the Blind Separation of Partially Correlated Sources},
  author={J. Bobin and J. Rapin and A. Larue and Jean-Luc Starck},
  journal={IEEE Transactions on Signal Processing},
  year={2015},
  volume={63},
  pages={1199-1213}
}
Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological diversity, to distinguish between the sources. However, dealing with real-world data reveals that such assumptions are rarely valid in practice: the signals of interest are more… Expand
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