Acoustic Feature Extraction for a Neural Network Classifier.

Abstract

Artificial neural networks can perform reliable classification of ground vehicles based solely on their acoustic signatures, if robust features can be identified. We present feature extraction and classification results using simple power spectrum estimates, harmonic line association, and principal component analysis. Algorithm implementation and performance analysis of each feature extraction method are discussed. Also given are preliminary evaluation results of a VLSI (very-large-scale integration) device dedicated to neural network implementation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1997
Accession Number
ADA320924

Entities

People

  • David B. Hillis
  • Mark C. Wellman
  • Nassy Srour

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Sets
  • Dimensionality Reduction
  • Extraction
  • Factor Analysis
  • Feature Extraction
  • Fluid Mechanics
  • Frequency
  • Ground Vehicles
  • Information Science
  • Machine Learning
  • Neural Networks
  • Power Spectra
  • Signal Processing
  • Spectra
  • Very Large Scale Integration

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Integrated Circuit Design and Technology.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks