@TECHREPORT\{IMM2006-04874,
    author       = "P. C. Hansen and S. H. Jensen",
    title        = "Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions",
    year         = "2006",
    keywords     = "Rank reduction, subspace methods, noise reduction, speech processing, {SVD,} {GSVD,} rank-revealing decompositions, {FIR} filter interpretation, canonical filters.",
    number       = "",
    series       = "IMM-Technical Report-2006-18",
    institution  = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4874-full.html",
    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."
}