Reference : Improved Non-Linear Long-Term Predictors based on Volterra Filters
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/40928
Improved Non-Linear Long-Term Predictors based on Volterra Filters
English
Despotovic, Vladimir mailto [University of Belgrade > Technical Faculty in Bor]
Goertz, Norbert [Technische Universit├Ąt Wien = Vienna University of Technology - TU Vienna > Institute of Telecommunications]
Peric, Zoran [University of Nis > Faculty of Electronic Engineering]
Sep-2012
Proceedings ELMAR-2012
IEEE
231-234
Yes
978-953-7044-14-5
54th International Symposium ELMAR 2012
from 12-09-2012 to 14-09-2012
Zadar
Croatia
[en] Volterra filters ; Speech prediction ; Pitch ; Nonlinear signal processing
[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.
OeAD-GmbH
http://hdl.handle.net/10993/40928
https://ieeexplore.ieee.org/document/6338513?arnumber=6338513&contentType=Conference%20Publications

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