Investigation of a Polynomial Matrix Generalised EVD for Multi-Channel Wiener Filtering

被引:0
|
作者
Corr, Jamie [1 ]
Pestana, Jennifer [2 ]
Weiss, Stephan [1 ]
Proudler, Ian K. [1 ,3 ]
Redif, Soydan [4 ]
Moonen, Marc [5 ]
机构
[1] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow, Lanark, Scotland
[2] Univ Strathclyde, Dept Math & Stat, Glasgow, Lanark, Scotland
[3] Loughborough Univ, Sch Mech Elect & Mfg Engn, Loughborough, Leics, England
[4] European Univ Lefke, Elect & Elect Engn Dept, Lefke, Cyprus
[5] Katholieke Univ Leuven, Dept Elect Engn, Leuven, Belgium
基金
英国工程与自然科学研究理事会;
关键词
LOW-RANK APPROXIMATION; ALGORITHMS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
State-of-the-art narrowband noise cancellation techniques utilise the generalised eigenvalue decomposition (GEVD) for multi-channel Wiener filtering, which can be applied to independent frequency bins in order to achieve broadband processing. Here we investigate the extension of the GEVD to broadband, polynomial matrices, akin to strategies that have already been developed by McWhirter et. al on the polynomial matrix eigenvalue decomposition (PEVD). In our approach we extend the Cholesky method for calculating the scalar GEVD to polynomial matrices. In this paper we outline our Cholesky-like approach, which utilises recently developed techniques for polynomial matrix spectral factorisation and polynomial matrix inversion.
引用
收藏
页码:1354 / 1358
页数:5
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