A chaotic time series prediction model for speech signal encoding based on genetic programming
Created by W.Langdon from
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- @Article{Yang:2016:ASC,
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author = "Lei Yang and Junxi Zhang and Xiaojun Wu and
Yumei Zhang and Jingjing Li",
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title = "A chaotic time series prediction model for speech
signal encoding based on genetic programming",
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journal = "Applied Soft Computing",
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volume = "38",
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pages = "754--761",
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year = "2016",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2015.10.003",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494615006183",
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abstract = "In this paper, a novel solving method for speech
signal chaotic time series prediction model was
proposed. A phase space was reconstructed based on
speech signal's chaotic characteristics and the genetic
programming (GP) algorithm was introduced for solving
the speech chaotic time series prediction models on the
phase space with the embedding dimension m and time
delay tau. And then, the speech signal's chaotic time
series models were built. By standardized processing of
these models and optimizing parameters, a speech
signal's coding model of chaotic time series with
certain generalization ability was obtained. At last,
the experimental results showed that the proposed
method can get the speech signal chaotic time series
prediction models much more effectively, and had a
better coding accuracy than linear predictive coding
(LPC) algorithms and neural network model.",
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keywords = "genetic algorithms, genetic programming, Chaotic time
series prediction, Nonlinear coding model",
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notes = "School of Automation, Northwestern Polytechnical
University, Xi'an 710072, China",
- }
Genetic Programming entries for
Pengfei Wen
Junxi Zhang
Xiaojun Wu
Yumei Zhang
Jingjing Li
Citations