Talks and Poster Presentations (with Proceedings-Entry):
V. Despotovic, N. Görtz, Z. Peric:
"Improved non-linear long-term predictors based on Volterra filters";
Talk: ELMAR 2012,
Zadar, Croatia;
09-12-2012
- 09-14-2012; in: "Proceedings ELMAR 2012",
IEEE Xplore,
(2012),
ISBN: 978-1-4673-1243-1;
231
- 234.
English abstract:
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.
German abstract:
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.
Keywords:
Nonlinear signal processing , Pitch , Speech prediction , Volterra filters
Electronic version of the publication:
http://publik.tuwien.ac.at/files/PubDat_211710.pdf
Created from the Publication Database of the Vienna University of Technology.