An iterative estimation technique for power spectra by an ARMA model

Spectral pole-zero models are used as a method of reducing the number of parameters needed to represent certain classes of spectra, e.g., spectral envelopes having deep valleys. We present here an iterative algorithm for Autoregressive-Moving Average (ARMA) models that is based on a repeated use of Levinson´s algorithm to obtain both poles and zeros. The procedure allows one to increase the number of poles and zeros iteretively, similar to linear prediction methods. Empirical results indicate that a slow growth of the orders of the denominator and numerator polynomials usually produces better ARMA models.