A Hilbert Space Approach to Linear Predictive Analysis of Speech Signals.

Abstract

Linear predictive analysis is geometrically interpreted to provide insight into the various formulations prevalent in current literature. The procedure for obtaining the predictive filter coefficients is considered as a minimum norm problem in an appropriate Hilbert space. Application of the projection theorem using specific sets of bases yields the normal equations for the covariance and autocorrelation methods. Orthogonalization of the basis vectors leads to the popular ladder structure and yields a recursive algorithm for evaluating the predictor and PARCOR coefficients. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1974
Accession Number
AD0776594

Entities

People

  • Allen M. Peterson
  • Kishan Shenoi
  • Madihally J. Narasimha

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Autocorrelation
  • Coefficients
  • Covariance
  • Data Science
  • Equations
  • Hilbert Space
  • Information Science
  • Literature
  • Mathematical Analysis
  • Mathematics
  • Statistical Algorithms

Readers

  • Approximation Theory.
  • Computational Fluid Dynamics (CFD)

Technology Areas

  • Space