Adaptive time-varying parametric modeling

We propose an adaptive procedure to model non-stationary signals using autoregressive systems with time-varying parameters. A non-stationary signal that is representable by a time-varying autoregressive system has parameters which are expandable in terms of a set of basis functions. The parameters can be found by posing a minimum least-squares modeling problem and solving a large set of normal equations. The costly calculations involved in this problem make an adaptive solution quite desirable. Using the parameter expansions, we convert the modeling into a linear prediction problem and solve it adaptively for a given set of basis functions. We apply our procedure in the modeling of a segment of speech and in the estimation of the evolutionary spectrum of a non-stationary signal