LI Jingjing, ZHANG Qijin, ZHANG Yumei, WU Xiaojun, WANG Xiaoming, SU Yuping. Hidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals[J]. Chinese Journal of Electronics, 2018, 27(6): 1221-1228. doi: 10.1049/cje.2018.09.010
Citation: LI Jingjing, ZHANG Qijin, ZHANG Yumei, WU Xiaojun, WANG Xiaoming, SU Yuping. Hidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals[J]. Chinese Journal of Electronics, 2018, 27(6): 1221-1228. doi: 10.1049/cje.2018.09.010

Hidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals

doi: 10.1049/cje.2018.09.010
Funds:  This work is supported by the National Key Research and Development Program of China (No.2017YFB1402100), the National Natural Science Foundation of China (No.11772178, No.11502133, No.61701291), the 111 Project (No.B16031), the Fundamental Research Fund for the Central Universities (No.2017CBY008), the China Postdoctoral Science Foundation funded project (No.2017M613053), and the Shaanxi Science & Technology Co-ordination & Innovation Project (No.2015KTZDGY06-05-01).
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  • Corresponding author: WU Xiaojun (corresponding author) is a professor in Shaanxi Normal University, Xi'an, China. His research interests include pattern recognition, intelligent system and system complexity. (Email:xjwu@snnu.edu.cn)
  • Received Date: 2018-04-23
  • Rev Recd Date: 2018-05-17
  • Publish Date: 2018-11-10
  • Speech signals are nonlinear chaotic time series. This paper proposes a novel speech signal nonlinear prediction model with the hidden phase space reconstruction method. The parameters, embedding dimension m, time delay τ and model structure are solved simultaneously, breaking the restriction of phase space, which needs to be reconstructed before modeling for the existing prediction method. Subsequently, an explicit speech signal prediction model is generated. Meanwhile, the introduction of the frame length parameter k effectively extends the prediction length. Experimental results show that the values of m and τ solved by the proposed method are consistent with the values addressed by the Cao method and mutual information method, respectively. In addition, the optimal value of k is further discussed. The prediction results obtained using the proposed model are more accurate than those of linear prediction coding, the radial basis function neural network model and the long short-term memory network.
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