@article{oai:iwate-u.repo.nii.ac.jp:00010292,
 author = {NISHIYAMA, Kiyoshi},
 journal = {IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences},
 month = {Feb},
 note = {A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.},
 pages = {547--556},
 title = {Time-Varying AR Spectral Estimation Using an Indefinite Matrix-Based Sliding Window Fast Linear Prediction},
 volume = {E97-A},
 year = {2014}
}