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Collaborative sources identification in mixed signals via hierarchical sparse modeling

Publication ,  Journal Article
Sprechmann, P; Ramirez, I; Cancela, P; Sapiro, G
Published in: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
August 18, 2011

A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dictionaries, each one of them learned to sparsely model one of a set of possible classes. Then, the coding of the mixed signal is performed by efficiently solving a convex optimization problem that combines standard sparsity with group and collaborative sparsity. The present sources are identified by looking at the sub-dictionaries automatically selected in the coding. The collaborative filtering in C-HiLasso takes advantage of the temporal/spatial redundancy in the mixed signals, letting collections of samples collaborate in identifying the classes, while allowing individual samples to have different internal sparse representations. This collaboration is critical to further stabilize the sparse representation of signals, in particular the class/sub-dictionary selection. The internal sparsity inside the sub-dictionaries, as naturally incorporated by the hierarchical aspects of C-HiLasso, is critical to make the model consistent with the essence of the sub-dictionaries that have been trained for sparse representation of each individual class. We present applications from speaker and instrument identification and texture separation. In the case of audio signals, we use sparse modeling to describe the short-term power spectrum envelopes of harmonic sounds. The proposed pitch independent method automatically detects the number of sources on a recording. © 2011 IEEE.

Duke Scholars

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

DOI

ISSN

1520-6149

Publication Date

August 18, 2011

Start / End Page

5816 / 5819
 

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Sprechmann, P., Ramirez, I., Cancela, P., & Sapiro, G. (2011). Collaborative sources identification in mixed signals via hierarchical sparse modeling. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5816–5819. https://doi.org/10.1109/ICASSP.2011.5947683
Sprechmann, P., I. Ramirez, P. Cancela, and G. Sapiro. “Collaborative sources identification in mixed signals via hierarchical sparse modeling.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, August 18, 2011, 5816–19. https://doi.org/10.1109/ICASSP.2011.5947683.
Sprechmann P, Ramirez I, Cancela P, Sapiro G. Collaborative sources identification in mixed signals via hierarchical sparse modeling. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2011 Aug 18;5816–9.
Sprechmann, P., et al. “Collaborative sources identification in mixed signals via hierarchical sparse modeling.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Aug. 2011, pp. 5816–19. Scopus, doi:10.1109/ICASSP.2011.5947683.
Sprechmann P, Ramirez I, Cancela P, Sapiro G. Collaborative sources identification in mixed signals via hierarchical sparse modeling. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2011 Aug 18;5816–5819.

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

DOI

ISSN

1520-6149

Publication Date

August 18, 2011

Start / End Page

5816 / 5819