Time signal filtering by relative neighborhood graph localized linear approximation

John Aasted Sørensen

doi:10.1109/NNSP.1994.366051

Time signal filtering by relative neighborhood graph localized linear approximation

John Aasted Sørensen

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Abstract

A time signal filtering algorithm based on the relative neighborhood graph (RNG) used for localization of linear filters is proposed. The filter is constructed from a training signal during two stages. During the first stage an RNG is constructed. During the second stage, localized linear filters are associated each RNG node and adapted to the training signal. The filtering of a test signal is then carried out by inserting the test signal vectors in the RNG followed by the determination of the filter output as a function of the linear filters or the RNG nodes to which the vectors are associated. Training examples are given on a segment of a speech signal and a signal with burst structure generated from a bilinear Subba Rao model

Original language	English
Title of host publication	Proceedings of the 4th IEEE Workshop Neural Networks for Signal Processing
Publisher	IEEE
Publication date	1994
Pages	171-176
ISBN (Print)	07-80-32026-3
DOIs	https://doi.org/10.1109/NNSP.1994.366051
Publication status	Published - 1994
Event	1994 IEEE Workshop on Neural Networks for Signal Processing - Ermoino, Greece Duration: 6 Sept 1994 → 8 Sept 1994 Conference number: 4 https://ieeexplore.ieee.org/xpl/conhome/2959/proceeding http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=2959

Conference

Conference	1994 IEEE Workshop on Neural Networks for Signal Processing
Number	4
Country/Territory	Greece
City	Ermoino
Period	06/09/1994 → 08/09/1994
Internet address	https://ieeexplore.ieee.org/xpl/conhome/2959/proceeding http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=2959

Bibliographical note

Copyright: 1994 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

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title = "Time signal filtering by relative neighborhood graph localized linear approximation",

abstract = "A time signal filtering algorithm based on the relative neighborhood graph (RNG) used for localization of linear filters is proposed. The filter is constructed from a training signal during two stages. During the first stage an RNG is constructed. During the second stage, localized linear filters are associated each RNG node and adapted to the training signal. The filtering of a test signal is then carried out by inserting the test signal vectors in the RNG followed by the determination of the filter output as a function of the linear filters or the RNG nodes to which the vectors are associated. Training examples are given on a segment of a speech signal and a signal with burst structure generated from a bilinear Subba Rao model",

author = "S{\o}rensen, {John Aasted}",

note = "Copyright: 1994 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; 1994 IEEE Workshop on Neural Networks for Signal Processing, NNSP 1994 ; Conference date: 06-09-1994 Through 08-09-1994",

year = "1994",

doi = "10.1109/NNSP.1994.366051",

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AB - A time signal filtering algorithm based on the relative neighborhood graph (RNG) used for localization of linear filters is proposed. The filter is constructed from a training signal during two stages. During the first stage an RNG is constructed. During the second stage, localized linear filters are associated each RNG node and adapted to the training signal. The filtering of a test signal is then carried out by inserting the test signal vectors in the RNG followed by the determination of the filter output as a function of the linear filters or the RNG nodes to which the vectors are associated. Training examples are given on a segment of a speech signal and a signal with burst structure generated from a bilinear Subba Rao model

U2 - 10.1109/NNSP.1994.366051

DO - 10.1109/NNSP.1994.366051

M3 - Article in proceedings

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BT - Proceedings of the 4th IEEE Workshop Neural Networks for Signal Processing

PB - IEEE

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ER -

Time signal filtering by relative neighborhood graph localized linear approximation

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