@book{d426b8e62ef346489969c20b47f7de1d,
title = "Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions",
abstract = "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.",
keywords = "SVD, canonical filters., FIR filter interpretation, rank-revealing decompositions, subspace methods, Rank reduction, GSVD, noise reduction, speech processing",
author = "Hansen, {Per Christian} and Jensen, {S{\o}ren Holdt}",
year = "2006",
language = "English",
publisher = "Informatics and Mathematical Modelling, Technical University of Denmark, DTU",
}