In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.
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ASME 2008 Noise Control and Acoustics Division Conference
July 28–30, 2008
Dearborn, Michigan, USA
Conference Sponsors:
- Noise Control and Acoustics Division
ISBN:
0-7918-4839-6
PROCEEDINGS PAPER
An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier
Brian Bucci
,
Brian Bucci
University of Pittsburgh, Pittsburgh, PA
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Jeffrey Vipperman
Jeffrey Vipperman
University of Pittsburgh, Pittsburgh, PA
Search for other works by this author on:
Brian Bucci
University of Pittsburgh, Pittsburgh, PA
Jeffrey Vipperman
University of Pittsburgh, Pittsburgh, PA
Paper No:
NCAD2008-73046, pp. 215-224; 10 pages
Published Online:
June 22, 2009
Citation
Bucci, B, & Vipperman, J. "An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier." Proceedings of the ASME 2008 Noise Control and Acoustics Division Conference. ASME 2008 Noise Control and Acoustics Division Conference. Dearborn, Michigan, USA. July 28–30, 2008. pp. 215-224. ASME. https://doi.org/10.1115/NCAD2008-73046
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