Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis

Per Christian Hansen, Søren Holdt Jensen

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated with working Matlab code and applications in speech processing.
    Original languageEnglish
    JournalEURASIP Journal on Applied Signal Processing
    Volume2007
    Pages (from-to)092953
    Number of pages24
    ISSN1110-8657
    DOIs
    Publication statusPublished - 2007

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