Phase-optimized K-SVD for signal extraction from underdetermined multichannel sparse mixtures

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Details zur Publikation

Autor(en): Deleforge A, Kellermann W
Jahr der Veröffentlichung: 2015
Seitenbereich: 355-359
ISBN: 978-1-4673-6997-8
Sprache: Englisch


Abstract


We propose a novel sparse representation for heavily underdetermined multichannel sound mixtures, i.e., with much more sources than microphones. The proposed approach operates in the complex Fourier domain, thus preserving spatial characteristics carried by phase differences. We derive a generalization of K-SVD which jointly estimates a dictionary capturing both spectral and spatial features, a sparse activation matrix, and all instantaneous source phases from a set of signal examples. This dictionary can be used to extract the learned signal from a new input mixture. The method is applied to the challenging problem of ego-noise reduction for robot audition. We demonstrate its superiority relative to conventional dictionary-based techniques using real-room recordings.


FAU-Autoren / FAU-Herausgeber

Deleforge, Antoine
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


Zitierweisen

APA:
Deleforge, A., & Kellermann, W. (2015). Phase-optimized K-SVD for signal extraction from underdetermined multichannel sparse mixtures. (pp. 355-359). Brisbane, AU.

MLA:
Deleforge, Antoine, and Walter Kellermann. "Phase-optimized K-SVD for signal extraction from underdetermined multichannel sparse mixtures." Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane 2015. 355-359.

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Zuletzt aktualisiert 2018-17-10 um 06:53