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Sparse Multichannel Source Localization and Separation
University College Dublin, Ireland.ORCID iD: 0000-0002-3912-1470
2008 (English)In: / [ed] The Institute of Mathematics and its Applications, Cirencester: The Institute of Mathematics and its Applications , 2008, 90-93 p.Conference paper (Refereed)
Abstract [en]

The DUET and DESPRIT blind source separation algorithms attempt to recover J sources from I mixtures of these sources, in the interesting case where J > I, with minimal information about the mixing environment of underling sources statistics. We present a semi-blind generalization of the DUET-DESCRIPT approach which allows arbitary placement of the sensors and demixes the sources given the room impulse response. We learn a sparse representation of the mixtures on an over-complete spatial signatures dictionary. We localize and separate the constituent sources via binary masking of a power weighted histogram in location space or in attenuation-delay space. We demonstrate the robustness of this technique using synthetic room experiments.

Place, publisher, year, edition, pages
Cirencester: The Institute of Mathematics and its Applications , 2008. 90-93 p.
Keyword [en]
Sparse Signal Processing; Blind Source Separation; Time-Frequency Analysis
National Category
Signal Processing
Research subject
Applied and Computational Mathematics
URN: urn:nbn:se:kth:diva-175440OAI: diva2:860976
8th International Conference on Mathematics in Signal Processing

QC 20151216

Available from: 2015-10-14 Created: 2015-10-14 Last updated: 2015-12-16Bibliographically approved

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de Fréin, Ruairí
Signal Processing

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