| Description |
Autocorrelation Pole-Zero modeling identifies the parameters of a rational transfer function H(z) whose short time-lag autocorrelations either exactly match (Autocorrelation partial Realization) or closely approximate (Autocorrelation Prediction) those of a given spectrum. As a result, the spectrum of the H(z) obtained from either method approximates the gross structure of the given spectrum. Autocorrelation Partial Realization (APR) uses the Pade approximation to determine the denominator coefficients of H(z). To compute the numerator coefficients of H(z), APR uses an iterative algorithm such as Fejer's or Newton-Raphson's. In contrast, Autocorrelation Prediction (AP) uses only Linear Prediction (LP) to determine both the denominator and numerator coefficients. Therefore, once the autocorrelation function of the given spectrum is known, AP uses only linear operations and no Fourier Transformations to determine the parameters of H(z). Moreover, the resulting rational transfer function is guaranteed to be minimum phase and consequently stable . AP can also automatically determine the least (parsimonious) denominator and numerator orders required to model efficiently a given spectral envelope. A dynamic filtering process, based on Wiener filtering and Autocorrelation Prediction, was developed to suppress the background noise from degraded speech. More important, using AP, a Linear Predictive Vocoder was integrated into the so called "Pole-Zero Vocoder"(PZV). Computer simulations of both, the dynamic filtering process and the PZV were successfully used in speech processing. |
