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

Publication: Research - peer-reviewJournal article – Annual report year: 2007

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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 Advances in Signal Processing
Publication date2007
Volume2007
Pages092953
Number of pages24
ISSN1110-8657
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 2
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