Jacobi SVD algorithms for tracking of nonstationary signals

In this paper we consider the algorithm for SVD updating based on Jacobi rotations. In order to overcome the tradeoff between accuracy and updating rate intrinsic in the original algorithm, we propose two schemes which improve the overall performance when the rate of change of the data is high. In the “variable rotational rate” scheme, the number of Jacobi rotations per update is dynamically determined. In the “variable forgetting factor” approach, the effective width of the observation adjusts to the data nonstationarity. The former scheme ensures closeness to convergence at all times, while the latter adapts the response to data variation. We consider applications of the SVD updating algorithm to speech processing of segmentation, adaptive parameter estimation, and glottal closure detection