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Artificial Neural Network Military Impulse Noise Classifier

[+] Author Affiliations
Brian Bucci, Jeffrey Vipperman

University of Pittsburgh

Paper No. IMECE2006-14065, pp. 5-10; 6 pages
  • ASME 2006 International Mechanical Engineering Congress and Exposition
  • Noise Control and Acoustics
  • Chicago, Illinois, USA, November 5 – 10, 2006
  • Conference Sponsors: Noise Control and Acoustics Division
  • ISBN: 0-7918-4776-4 | eISBN: 0-7918-3790-4
  • Copyright © 2006 by ASME


Civilian noise complaints and damage claims have created the need for stations to monitor the production of military impulse noise. However, these stations suffer from numerous false positive detections (due to wind noise) of impulse events and often miss many events of interest. There is also interest in identifying specific noise sources, such different types of ordinance or different types of aircraft. To improve the accuracy of military impulse noise monitoring and make and initial effort to specifically classify noise source, an algorithm based upon an artificial neural network with inputs of conventional and custom acoustic metrics was proposed. To train and evaluate the noise classifier approximately 1,000 waveforms were field collected (110 military aircraft noise, 330 military impulse noise, and 560 non-impulse noise). The final noise classifier used kurtosis and crest factor and the custom metrics spectral slope and weighted square error as inputs. The classifier was able to achieve 99.7% accuracy on the training data set and 99.4% accuracy on the validation data set.

Copyright © 2006 by ASME



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