Imperial College London
Alternate

Patrick A. Naylor

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Speech & Acoustic Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6235p.naylor Website

 
 
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Location

 

803Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Neo:2019:10.1109/ICASSP.2019.8683425,
author = {Neo, V and Naylor, PA},
doi = {10.1109/ICASSP.2019.8683425},
pages = {8043--8047},
publisher = {IEEE},
title = {Second order sequential best rotation algorithm with householder reduction for polynomial matrix eigenvalue decomposition},
url = {http://dx.doi.org/10.1109/ICASSP.2019.8683425},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The Second-order Sequential Best Rotation (SBR2) algorithm, usedfor Eigenvalue Decomposition (EVD) on para-Hermitian polynomialmatrices typically encountered in wideband signal processingapplications like multichannel Wiener filtering and channel coding,involves a series of delay and rotation operations to achieve diagonalisation.In this paper, we proposed the use of Householder transformationsto reduce polynomial matrices to tridiagonal form beforezeroing the dominant element with rotation. Similar to performingHouseholder reduction on conventional matrices, our methodenables SBR2 to converge in fewer iterations with smaller orderof polynomial matrix factors because more off-diagonal Frobeniusnorm(F-norm) could be transferred to the main diagonal at everyiteration. A reduction in the number of iterations by 12.35% and0.1% improvement in reconstruction error is achievable.
AU  - Neo,V
AU  - Naylor,PA
DO  - 10.1109/ICASSP.2019.8683425
EP  - 8047
PB  - IEEE
PY  - 2019///
SN  - 0736-7791
SP  - 8043
TI  - Second order sequential best rotation algorithm with householder reduction for polynomial matrix eigenvalue decomposition
UR  - http://dx.doi.org/10.1109/ICASSP.2019.8683425
UR  - http://hdl.handle.net/10044/1/68312
ER  -
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