Blind Separation of Sources with Sparse Representations in a given Signal Dictionary

@inproceedings{Zibulevsky2001BlindSO,
  title={Blind Separation of Sources with Sparse Representations in a given Signal Dictionary},
  author={Zibulevsky and Barak A. Pearlmutter},
  year={2001}
}
The blind sour e separation problem is to extra t the underlying sour e signals from a set of linear mixtures, where the mixing matrix is unknown. We onsider a two-stage separation pro ess. First, a priori sele tion of a possibly over omplete signal di tionary (e.g. wavelet frame, learned di tionary, et .) in whi h the sour es are assumed to be sparsely representable. Se ond, unmixing the sour es by exploiting the their sparse representability. We onsider the general ase of more sour es than… CONTINUE READING
Highly Cited
This paper has 19 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 14 extracted citations

Recognizing faces with PCA and ICA

Computer Vision and Image Understanding • 2003
View 8 Excerpts
Highly Influenced

Blind Source Separation of Audios Based on Bayesian Method

2010 International Conference on Multimedia Information Networking and Security • 2010
View 1 Excerpt

Appearance-Based Subspace Projection Techniques for Face Recognition

2009 International Asia Symposium on Intelligent Interaction and Affective Computing • 2009
View 1 Excerpt

Appearance-based statistical methods for face recognition

47th International Symposium ELMAR, 2005. • 2005
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 14 references

Sparse underdetermined ICA: Estimating the mixing matrix and the sour es separately,

P. Bo ll, M. Zibulevsky
Te h. Rep. UPC-DAC-20007, Universitat Polite ni a de Catalunya, • 2000

A probabilisti framework for the adaptation and omparison of image odes,

M. Lewi ki, B. Olshausen
Journal of the Opti al So iety of Ameri a, • 1999

Hyvarinen, \Fast and robust xed-point algorithms for independent omponent analysis,

IEEE Transa tions on Neural Networks, • 1999

ICA/EEG toolbox.

S. Makeig
Computational Neurobiology Laboratory, the Salk Institute, • 1999

A Wavelet Tour of Signal Pro essing

S. Mallat
A ademi Press, • 1998

Blind sour e separation of more sour es than mixtures using over omplete representations,

T. Lee, M. Lewi ki, M. Girolami, T. Sejnowski
IEEE Sig. Pro . Lett., • 1998

Hyvarinen, \The Fast-ICA MATLAB pa kage,

1998

Learning over omplete representations,

M. Lewi ki, T. Sejnowski
Neural Computation, • 1998

Similar Papers

Loading similar papers…