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Microphone Array Analysis Methods Using Cross-Correlations

[+] Author Affiliations
Matthew Rhudy, Brian Bucci, Jeffrey Vipperman

University of Pittsburgh, Pittsburgh, PA

Jeffrey Allanach, Bruce Abraham

Applied Physical Sciences Corp., Groton, CT

Paper No. IMECE2009-10798, pp. 281-288; 8 pages
doi:10.1115/IMECE2009-10798
From:
  • ASME 2009 International Mechanical Engineering Congress and Exposition
  • Volume 15: Sound, Vibration and Design
  • Lake Buena Vista, Florida, USA, November 13–19, 2009
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4388-8 | eISBN: 978-0-7918-3863-1
  • Copyright © 2009 by ASME

abstract

Due to civilian noise complaints and damage claims, there is a need to establish an accurate record of impulse noise generated at military installations. Current noise monitoring systems are susceptible to false positive detection of impulse events due to wind noise. In order to analyze the characteristics of noise events, multiple channel data methods were investigated. A microphone array was used to collect four channel data of military impulse noise and wind noise. These data were then analyzed using cross-correlation functions to characterize the input waveforms. Four different analyses of microphone array data are presented. A new value, the min peak correlation coefficient, is defined as a measure of the likelihood that a given waveform originated from a correlated noise source. Using a sound source localization technique, the angle of incidence of the noise source can be calculated. A method was also developed to combine the four individual microphone channels into one. This method aimed to preserve the correlated part of the overall signal, while minimizing the effects of uncorrelated noise, such as wind. Lastly, a statistical method called the acoustic likelihood test is presented as a method of determining if a signal is correlated or not.

Copyright © 2009 by ASME

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