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

Deleforge A, Kellermann W (2015)

Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2015

Pages Range: 355-359

Article Number: 7177990

Event location: Brisbane AU

ISBN: 978-1-4673-6997-8

DOI: 10.1109/ICASSP.2015.7177990

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.

Authors with CRIS profile

Antoine Deleforge Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

How to cite

APA:

Deleforge, A., & Kellermann, W. (2015). Phase-optimized K-SVD for signal extraction from underdetermined multichannel sparse mixtures. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (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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