A New Parametric Adaptive Nonstationarity Detector and Application

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.