Voice Analysis Using the Bispectrum.

Abstract

The theory of the bispectrum has been studied, though very few practical applications have yet been considered in any depth. One application mentioned in the literature is the use of the bispectrum for voice signal processing. The aim of this thesis was to research the bispectrum towards the particular application of speech enhancement. The technique is based on the fact that the bispectrum is zero for a Gaussian white noise signal, arid the bispectrum of two signals added together is the sum of the two signal bispectra. Theoretically, processing signals in the bispectra domain should increase the signal-to-noise ratio of the speech signal. The signal can then be reconstructed from the bispectrum. Though the theory of the estimation techniques were proven, the applicability of the bispectrum to voice processing was questionable. Since any additive white noise is a random process, it will only be the expected value that is zero. With speech signal, the signal is considered stationary for only approximately 20 milliseconds. This does not allow a significant amount of the noise energy to be removed through the averaging process. Classical methods are just as effective.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1994
Accession Number
ADA289204

Entities

People

  • Deborah A. Douglass

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computer Programming
  • Data Science
  • Databases
  • Electrical Engineering
  • Estimators
  • Frequency
  • Gaussian Processes
  • Information Processing
  • Information Science
  • Least Squares Method
  • Noise
  • Order Statistics
  • Power Spectra
  • Random Variables
  • Signal Processing
  • Three Dimensional
  • White Noise

Fields of Study

  • Engineering

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Radio communications and signal processing.
  • Regression Analysis.