MURAL - Maynooth University Research Archive Library



    The LOST algorithm: finding lines and separating speech mixtures


    O'Grady, Paul D. and Pearlmutter, Barak A. (2008) The LOST algorithm: finding lines and separating speech mixtures. EURASIP Journal on Advances in Signal Processing . ISSN 1687-6172

    [img] Download (6MB)


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Robust clustering of data into linear subspaces is a frequently encountered problem. Here, we treat clustering of one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We propose the LOST algorithm, which identifies such subspaces using a procedure similar in spirit to EM. This line finding procedure combined with a transformation into a sparse domain and an L1-norm minimisation constitutes a blind source separation algorithm for the separation of instantaneous mixtures with an arbitrary number of mixtures and sources. We perform an extensive investigation on the general separation performance of the LOST algorithm using randomly generated mixtures, and empirically estimate the performance of the algorithm in the presence of noise. Furthermore, we implement a simple scheme whereby the number of sources present in the mixtures can be detected automatically.

    Item Type: Article
    Keywords: LOST algorithm; Separating speech mixtures; Laplacian distribution; Expectation-maximisation; Hamilton Institute;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 1727
    Depositing User: Hamilton Editor
    Date Deposited: 10 Dec 2009 15:12
    Journal or Publication Title: EURASIP Journal on Advances in Signal Processing
    Publisher: Hindawi Publishing Corporation
    Refereed: Yes
    URI:

      Repository Staff Only(login required)

      View Item Item control page

      Downloads

      Downloads per month over past year