Investigation of a polynomial matrix generalised EVD for multi-channel Wiener filtering

Jamie Corr; Jennifer Pestana; Stephan Weiss; Soydan  Redif; Marc Moonen

Investigation of a polynomial matrix generalised EVD for multi-channel Wiener filtering

Jamie Corr, Jennifer Pestana, Stephan Weiss, Soydan Redif, Marc Moonen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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Abstract

State of the art narrowband noise cancellation techniques utilise the generalised eigenvalue decomposition (GEVD) for multichannel 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).

Original language	English
Title of host publication	50th Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE
Publication status	Accepted/In press - 19 Jul 2016
Event	50th Asilomar Conference on Signals, Systems and Computers - Asilomar Conference Ground, Pacific Grove, CA, United States Duration: 6 Nov 2016 → 9 Nov 2016 http://www.asilomarsscconf.org/

Conference

Conference	50th Asilomar Conference on Signals, Systems and Computers
Abbreviated title	Asilomar 2016
Country/Territory	United States
City	Pacific Grove, CA
Period	6/11/16 → 9/11/16
Internet address	http://www.asilomarsscconf.org/

Keywords

narrowband noise cancellation techniques
generalised eigenvalue decomposition
multichannel Wiener filtering
independent frequency bins
polynomial matrices
polynomial matrix eigenvalue decomposition

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Corr-etal-ACSSC2016-Investigation-of-a-polynomial-matrix-generalised-evdAccepted author manuscript, 157 KB

Cite this

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title = "Investigation of a polynomial matrix generalised EVD for multi-channel Wiener filtering",

abstract = "State of the art narrowband noise cancellation techniques utilise the generalised eigenvalue decomposition (GEVD) for multichannel 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).",

keywords = "narrowband noise cancellation techniques, generalised eigenvalue decomposition, multichannel Wiener filtering, independent frequency bins, polynomial matrices, polynomial matrix eigenvalue decomposition",

author = "Jamie Corr and Jennifer Pestana and Stephan Weiss and Soydan Redif and Marc Moonen",

note = "Copyright 2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE; 50th Asilomar Conference on Signals, Systems and Computers, Asilomar 2016 ; Conference date: 06-11-2016 Through 09-11-2016",

year = "2016",

month = jul,

day = "19",

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T1 - Investigation of a polynomial matrix generalised EVD for multi-channel Wiener filtering

AU - Corr, Jamie

AU - Pestana, Jennifer

AU - Weiss, Stephan

AU - Redif, Soydan

AU - Moonen, Marc

N1 - Copyright 2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE

PY - 2016/7/19

Y1 - 2016/7/19

N2 - State of the art narrowband noise cancellation techniques utilise the generalised eigenvalue decomposition (GEVD) for multichannel 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).

AB - State of the art narrowband noise cancellation techniques utilise the generalised eigenvalue decomposition (GEVD) for multichannel 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).

KW - narrowband noise cancellation techniques

KW - generalised eigenvalue decomposition

KW - multichannel Wiener filtering

KW - independent frequency bins

KW - polynomial matrices

KW - polynomial matrix eigenvalue decomposition

UR - http://www.asilomarsscconf.org/

M3 - Conference contribution book

BT - 50th Asilomar Conference on Signals, Systems and Computers

PB - IEEE

T2 - 50th Asilomar Conference on Signals, Systems and Computers

Y2 - 6 November 2016 through 9 November 2016

ER -

Investigation of a polynomial matrix generalised EVD for multi-channel Wiener filtering

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Signal Processing Solutions for the Networked Battlespace

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Investigation of a polynomial matrix generalised EVD for multi-channel Wiener filtering

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

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Signal Processing Solutions for the Networked Battlespace

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