Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75843
Title: A new parametric adaptive nonstationarity detector and application
Authors: Chu, YJ 
Mak, CM 
Keywords: Adaptive nonstationarity detection
Wald test
Weighted maximum a posteriori
RLS
And adaptive model-order selection
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on signal processing, 2017, v. 65, no. 19, p. 5203-5214 How to cite?
Journal: IEEE transactions on signal processing 
Abstract: Techniques for hypothesis testing can be used to solve a broad class of nonstationarity detection problems, which is a key issue in a variety of applications. To achieve lower complexity and to deal with real-time detection in practical applications, we develop a new adaptive nonstationarity detector by exploiting a parametric model. A weighted maximum a posteriori (MAP) estimator is developed to estimate the parameters associated with the parametric model. We then derive a regularized Wald test from the weighted MAP estimate, which is adaptively implemented by a regularized recursive least squares (RLS) algorithm. Several important issues are discussed, including model order selection, forgetting factor and regularization parameter selection for RLS, and numerically stable implementation using QR decomposition, which are intrinsic parts of the proposed parametric adaptive detector. Simulation results are presented to illustrate the efficiency of the proposed nonstationarity detector, with adaptive estimation and automatic model selection, especially for "slowly varying" type of nonstationarity such as time-varying spectrums and speeches.
URI: http://hdl.handle.net/10397/75843
ISSN: 1053-587X
EISSN: 1941-0476
DOI: 10.1109/TSP.2017.2725222
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