TY - GEN
T1 - Improved non-linear long-term predictors based on Volterra filters
AU - Despotović, Vladimir
AU - Görtz, Norbert
AU - Perić, Zoran
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Nonlinear signal processing
KW - Pitch
KW - Speech prediction
KW - Volterra filters
UR - http://www.scopus.com/inward/record.url?scp=84871085705&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84871085705
SN - 9789537044138
T3 - Proceedings Elmar - International Symposium Electronics in Marine
SP - 231
EP - 234
BT - Proceedings ELMAR-2012 - 54th International Symposium ELMAR-2012
T2 - 54th International Symposium ELMAR-2012
Y2 - 12 September 2012 through 14 September 2012
ER -