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

Per Christian Hansen, Søren Holdt Jensen

AbstractWe 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.
TypeJournal paper [With referee]
JournalEURASIP Journal on Advances in Signal Processing
Year2007
Notedoi:10.1155/2007/92953
BibTeX data [bibtex]
IMM Group(s)Scientific Computing


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