Analysis of a Digital Technique for Frequency Transposition of Speech.

Abstract

Frequency transposition is the process of raising or lowering the frequency content (plitch) of an audio signal. The hearing impaired community has the greatest interest in the applications of frequency transposing. Though several analog and digital frequency transposing hearing aid systems have been built and tested, this thesis investigates a possible digital processing alternative. Pole shifting, in the z-domain, of an autoregressive (all pole) model of speech was proven to be a viable theory for changing frequency content. Since linear predictive coding (LPC) techniques are used to code, analyze and synthesize speech, with the resulting LPC coefficients related to the coefficients of an equivalent autoregressive model, a linear relationship between LPC coefficients and frequency tranposition is explored. This theoretical relationship is first established using a pure sine wave and then is extended into processing speech. The resulting speech synthesis experiments failed to substantiate the conjectures of this thesis. However, future research avenues are suggested that may lead toward a viable approach to transpose speech. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1985
Accession Number
ADA160832

Entities

People

  • V. Digirolamo

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • California
  • Coding
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Acquisition
  • Digital Filters
  • Electrical Engineering
  • Engineering
  • Frequency
  • Frequency Shift
  • Information Science
  • Language
  • Sine Waves
  • Speech
  • United States
  • Waveforms

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Computer Programming and Software Development.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference