This paper develops a new recursive nonstationarity detection method based on time-varying autoregressive (TVAR) modeling. A local likelihood estimation approach is introduced which gives more weights to observations near the current time instant but less to those distance apart. It thus allows the Wald test to be computed based on RLS-type algorithms with low computational cost. A reliable and efficient state regularized variable forgetting factor (VFF) QR decomposition (QRD)-based RLS (SR-VFF-QRRLS) algorithm is adopted for estimation for its asymptotically unbiased property and immunity to lacking of excitation. Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a certain window size. The effectiveness of the proposed algorithm is evaluated by testing vocal tract changes in real speech signals.
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机译：本文开发了一种基于时变自自回归（TVAR）建模的新递归非定性检测方法。介绍了本地似然估计方法，这使得更多的权重对当前时间瞬间附近的观察，但较少的距离。因此，它允许基于具有低计算成本的RLS型算法来计算沃尔德测试。采用可靠且有效的状态正则化变量遗忘系数（VFF）QR分解（QRD）的RLS（SR-VFF-QRRLS）算法用于估计其渐近无偏见的性质和缺乏激励的免疫力。 Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a某些窗口大小。通过测试真实语音信号中的声乐道变化来评估所提出的算法的有效性。
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