Time-dependent spectral analysis of nonstationary time series
Time-dependent spectral analysis of nonstationary time series
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DOI：
10.2307/2670062
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发表时间：
1998-12-01
影响因子：
3.7
通讯作者：
Adak, S
中科院分区：
文献类型：
--
作者：
Adak, S
非平稳随机时间序列的建模在语音处理、生物医学信号处理、地震学和故障检测等领域有着广泛的应用。这些场的数据通常被建模为具有突变的分段平稳过程，并且利用谱图研究了它们随时间变化的光谱特征。本文介绍了一类一般的分段局部平稳过程，它允许非平稳时间序列的谱特征发生突变和平滑变化。结果表明，这类过程表现为近似分段平稳过程，可用于模拟各种自然现象。针对这类过程，提出了一种估计时变谱的自适应分割方法。该分割方法利用二叉树和加窗谱对数据进行非参数自适应分割。仿真研究结果表明，该方法具有较好的适应频谱变化速率的能力。考虑了该方法在语音信号和地震数据中的应用。
Modeling of nonstationary stochastic time series has found wide applications in speech processing, biomedical signal processing, seismology, and failure detection. Data from these fields have often been modeled as piecewise stationary processes with abrupt changes, and their time-varying spectral features have been studied with the help of spectrograms. A general class of piecewise locally stationary processes is introduced here that allows both abrupt and smooth changes in the spectral characteristics of the nonstationary time series. It is shown that this class of processes behave as approximately piecewise stationary processes and can be used to model various naturally occuring phenomena. An adaptive segmentation method of estimating the time-dependent spectrum is proposed for this class of processes. The segmentation procedure uses binary trees and windowed spectra to nonparametrically and adaptively partition the data into approximately stationary intervals. Results of simulation studies demonstrate that the method has excellent ability to adapt to the rate at which the spectrum is changing. Applications of the method to speech signals and earthquake data are considered.