Pole-Zero Modeling and its Applications to Speech Processing

Abstract

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--APR or closely APPROXIMATE(Autocorrelation Prediction--AP) 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. APR uses the Pade approximation to determine the denominator coefficients of H(z). In contrast, (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. A dynamic filtering process, based on Wiener filtering and Autocorrelation Prediction, was developed to suppress the background noise from degraded speech.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1976
Accession Number
ADA038701

Entities

People

  • Mohammad Ali Atashroo

Organizations

  • University of Utah

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Algorithms
  • Background Noise
  • Coefficients
  • Computational Science
  • Computer Simulations
  • Computers
  • Contrast
  • Equations
  • Fourier Transformation
  • Frequency
  • Frequency Domain
  • Noise
  • Production Models
  • Shift Registers
  • Time Domain
  • Transfer Functions

Readers

  • Acoustics.
  • Approximation Theory.
  • Control Systems Engineering.