Publication: Research - peer-review › Report – Annual report year: 2006
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.
|Publisher||Informatics and Mathematical Modelling, Technical University of Denmark, DTU|
- SVD, canonical filters., FIR filter interpretation, rank-revealing decompositions, subspace methods, Rank reduction, GSVD, noise reduction, speech processing
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