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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.