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

Publication: Research - peer-reviewReport – Annual report year: 2006

Documents

View graph of relations

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
Publication date2006
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
StatePublished

Keywords

  • SVD, canonical filters., FIR filter interpretation, rank-revealing decompositions, subspace methods, Rank reduction, GSVD, noise reduction, speech processing
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 2824463