@ARTICLE\{IMM2007-04875,
    author       = "P. C. Hansen and S. H. Jensen",
    title        = "Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis",
    year         = "2007",
    journal      = "{EURASIP} Journal on Advances in Signal Processing",
    volume       = "",
    editor       = "",
    number       = "",
    publisher    = "",
    note         = "doi:10.1155/2007/92953",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4875-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."
}