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

  • Per Christian HansenEmail author
  • Søren Holdt Jensen
Open Access
Research Article
Part of the following topical collections:
  1. Numerical Linear Algebra in Signal Processing Applications

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 analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated with working Matlab code and applications in speech processing.

Keywords

Information Technology Quantum Information Speech Signal Noise Reduction Triangular Matrix 

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Copyright information

© P. C. Hansen and S. H. Jensen. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Informatics and Mathematical ModellingTechnical University of DenmarkLyngbyDenmark
  2. 2.Department of Electronic SystemsAalborg UniversityAalborgDenmark

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