Add to ORKGSpeech 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
Abstract. The speech production can be modeled by linear and nonlinear sys-tems. In this contributio...
When a conventional NLMS adaptive filter is used to predict a process, especially when predicting se...
Abstract-Prediction error filters which combine short-time predic-tion (formant prediction) with lon...
Models based on linear prediction have been used for several decades in different areas of speech si...
Linear predictive coding is probably the most frequently used technique in speech signal processing....
Recent studies have shown that the airflow in the vocal tract is highly unstable and oscillates betw...
The analysis of speech is usually based on linear models. In this contribution speech features are t...
The filter involving the adaptation scheme of Volterra Series Least Mean Square(VSLMS) algorithm is ...
Non-linear prediction can be based on Volterra series expansion with some benefits especially when t...
The linear prediction coding of speech is based in the assumption that the generation model is auto...
It is well known that the production of speech involves non linear phenomena. Classical algorithms o...
A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive no...
Analysis of speech signals can be performed with the aid of linear or nonlinear statistics using app...
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined rec...
Abstract. The speech production can be modeled by linear and nonlinear sys-tems. In this contributio...
When a conventional NLMS adaptive filter is used to predict a process, especially when predicting se...
Abstract-Prediction error filters which combine short-time predic-tion (formant prediction) with lon...
Models based on linear prediction have been used for several decades in different areas of speech si...
Linear predictive coding is probably the most frequently used technique in speech signal processing....
Recent studies have shown that the airflow in the vocal tract is highly unstable and oscillates betw...
The analysis of speech is usually based on linear models. In this contribution speech features are t...
The filter involving the adaptation scheme of Volterra Series Least Mean Square(VSLMS) algorithm is ...
Non-linear prediction can be based on Volterra series expansion with some benefits especially when t...
The linear prediction coding of speech is based in the assumption that the generation model is auto...
It is well known that the production of speech involves non linear phenomena. Classical algorithms o...
A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive no...
Analysis of speech signals can be performed with the aid of linear or nonlinear statistics using app...
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined rec...
Abstract. The speech production can be modeled by linear and nonlinear sys-tems. In this contributio...
When a conventional NLMS adaptive filter is used to predict a process, especially when predicting se...
Abstract-Prediction error filters which combine short-time predic-tion (formant prediction) with lon...