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



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.
KeywordsRank reduction, subspace methods, noise reduction, speech processing, SVD, GSVD, rank-revealing decompositions, FIR filter interpretation, canonical filters.
TypeTechnical report
Year2006
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
SeriesIMM-Technical Report-2006-18
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Scientific Computing