Sparse Multichannel Source Localization and Separation

Ruairí de Fréin, Scott T. Rickard, Barak A. Pearlmutter

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    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.
    Original languageEnglish
    Publication statusPublished - 2008
    Event8th International Conference on Mathematics in Signal Processing - IMA (The Institute of Mathematics and its Applications, 2008), Cirencester, UK, IMA (The Institute of Mathematics and its Applications, 2008), Cirencester, UK
    Duration: 01 Jan 2008 → …
    https://www.researchgate.net/publication/286418534_Sparse_Multichannel_Source_Localization_and_Separation

    Conference

    Conference8th International Conference on Mathematics in Signal Processing
    CityIMA (The Institute of Mathematics and its Applications, 2008), Cirencester, UK
    Period01/01/2008 → …
    Internet address

    Keywords

    • Blind Source Separation, Time Frequency Analysis, Localization; Fourier Analysis; Short Time Fourier Transform; Dictionary Learning.

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