Non-Stationary Signal Classification Using Joint Frequency Analysis

Abstract

Time-varying short-term spectral estimates have been successfully applied in many classification tasks. However, they are still insufficient for many non-stationary signals where time-varying information is useful. In this paper, we propose to improve the deficiencies of current short-term feature analysis by adding information to describe the time-varying behavior of the signals. Our proposed method, which is motivated by the human auditory system, can be applied to several non-stationary signal types. Real world communication signals were used for experimental verification. These experimental results, assessed with a conventional probabilistic classifier, showed significant improvement when the new features were added to short-term spectral estimates.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA436792

Entities

People

  • Jack Mclaughlin
  • James W. Pitton
  • Les E. Atlas
  • Somsak Sukittanon

Organizations

  • University of Washington

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acoustic Frequencies
  • Air Force Research Laboratories
  • Carrier Frequencies
  • Classification
  • Data Analysis
  • Data Science
  • Deficiencies
  • Electrical Engineering
  • Feature Extraction
  • Frequency
  • Frequency Shift
  • Machine Learning
  • Military Research
  • Modulation
  • Phase Shift
  • Recognition
  • Stationary

Fields of Study

  • Engineering

Readers

  • Neural Network Machine Learning.
  • Speech Processing/Speech Recognition.