In this section
@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}
}
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 -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom