Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Improved Non-Linear Long-Term Predictors based on Volterra Filters
Despotovic, Vladimir; Goertz, Norbert; Peric, Zoran
2012In Proceedings ELMAR-2012
Peer reviewed


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Keywords :
Volterra filters; Speech prediction; Pitch; Nonlinear signal processing
Abstract :
[en] Speech prediction is extensively based on linear models. However, components generated by nonlinear effects are also contained in speech signals, which is neglected using linear techniques. This paper presents long-term nonlinear predictor based on second-order Volterra filters that is shown to be superior to linear long-term predictor with only a minimal increase in complexity and the number of coefficients. It can be used connected in cascade with short-term linear predictor. The frame/subframe structure is proposed, where each frame is divided into four subframes. Second order Volterra long-term prediction is applied to each subframe separately.
Disciplines :
Computer science
Author, co-author :
Despotovic, Vladimir ;  University of Belgrade > Technical Faculty in Bor
Goertz, Norbert;  Technische Universität Wien = Vienna University of Technology - TU Vienna > Institute of Telecommunications
Peric, Zoran;  University of Nis > Faculty of Electronic Engineering
External co-authors :
Language :
Title :
Improved Non-Linear Long-Term Predictors based on Volterra Filters
Publication date :
September 2012
Event name :
54th International Symposium ELMAR 2012
Event place :
Zadar, Croatia
Event date :
from 12-09-2012 to 14-09-2012
Main work title :
Proceedings ELMAR-2012
Publisher :
Pages :
Peer reviewed :
Peer reviewed
Funders :
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since 11 November 2019


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