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Nonlinear Signal Decomposition Into Functional Series For Speech Recognition: A New Approach

机译：非线性信号分解为语音识别功能序列的一种新方法

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Krot Alexander M.; Tkachova Polina P.; Goncharov Boris A.;
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1. New Approach to Speech Signal Recognition Using Nonlinear Signal Decomposition by Measuring Wiener Kernels [J] . Alexander M. Krot, Boris A. Goncharov, Polina P. Tkachova International Journal of Smart Engineering System Design . 2002,第4期

机译：测量维纳核的非线性信号分解语音信号识别新方法
2. Empirical Fourier decomposition: An accurate signal decomposition method for nonlinear and non-stationary time series analysis [J] . Wei Zhou, Zhongren Feng, Y.F. Xu, Mechanical systems and signal processing . 2022,第Jana期

机译：经验傅里叶分解：非线性和非静止时间序列分析的准确信号分解方法
3. Identification of Dynamic Patterns of Speech-Evoked Auditory Brainstem Response Based on Ensemble Empirical Mode Decomposition and Nonlinear Time Series Analysis Methods [J] . Mozaffarilegha Marjan, Esteki Ali, Ahadi Mohsen, International journal of bifurcation and chaos in applied sciences and engineering . 2016,第12期

机译：基于集合经验模态分解和非线性时间序列分析方法的语音诱发听觉脑干反应动态模式识别
4. NONLINEAR SIGNAL DECOMPOSITION INTO FUNCTIONAL SERIES FOR SPEECH RECOGNITION: A NEW APPROACH [C] . Alexander M. Krot, Polina P. Tkachova, Boris A. Goncharov Signal Processing X: Theories and Applications . 2000

机译：语音识别功能性序列的非线性信号分解：一种新方法
5. Analysis and processing of nonlinear time series - from speech to neurophysiological signals. [D] . Sun, Junfeng. 2009

机译：非线性时间序列的分析和处理-从语音到神经生理信号。
6. The Fourier decomposition method for nonlinear and non-stationary time series analysis [O] . Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, -1

机译：非线性和非平稳时间序列分析的傅立叶分解方法
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Nonlinear Signal Decomposition Into Functional Series For Speech Recognition: A New Approach

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