Nonlinear speech coding model based on genetic programming

Authors:
Xiaojun Wu;Zhanzhong Yang
Affiliations:
Key Laboratory of Modern Teaching Technology (Shaanxi Normal University), Ministry of Education, Xi'an, Shaanxi 710062, China and School of Computer Science, Shaanxi Normal University, Xi'an, Shaa ...;College of Automation of Northwestern University, Xi'an, Shaanxi 710072, China
Venue:
Applied Soft Computing
Year:
2013

Citing 10
Cited 0

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Dissipative particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Parallel implementation of Artificial Neural Network training for speech recognition

Pattern Recognition Letters
Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

Knowledge-Based Systems
An adaptive waveform coding algorithm and its application in speech coding

Digital Signal Processing
Mathematical foundations of nonlinear, non-Gaussian, and time-varying digital speech signal processing

NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Suppression of Late Reverberation Effect on Speech Signal Using Long-Term Multiple-step Linear Prediction

IEEE Transactions on Audio, Speech, and Language Processing
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model

IEEE Transactions on Evolutionary Computation
Matrix analysis of genetic programming mutation

EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Bandwidth Extension of Telephone Speech Using a Neural Network and a Filter Bank Implementation for Highband Mel Spectrum

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

An improved genetic programming is proposed in this paper to construct the nonlinear models of speech signals, and the speech coding is further accomplished. After the preprocessing of the speech signals, the improved GP is used to construct the corresponding model of each speech frame. Then by analyzing these models, a normalized model that has generalization ability is obtained. And finally the process of speech coding is accomplished by the optimizing the parameters of the normalized model using an optimization algorithm. Experiments demonstrate that the feasibility of the improved GP in the modeling of speech signals, and show the superiority of the proposed method in speech coding based on the comparisons with the linear predictive coding.