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Development of artificial neural network-based classifiers to identify military impulse noise

Bucci, Brian Arthur (2008) Development of artificial neural network-based classifiers to identify military impulse noise. Master's Thesis, University of Pittsburgh. (Unpublished)

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Noise monitoring stations are in place around some military installations, to provide records that assist in processing noise complaints and damage claims. However, they are known to produce false positives and miss many impulse events. In this thesis, classifiers based on artificial neural networks were developed to improve the accuracy of military impulse noise identification. Two time-domain metrics, kurtosis and crest factor, and two custom frequency-domain metrics, spectral slope and weighted square error, were selected as inputs to the artificial neural networks. A separate effort attempted to identify military impulse noise by the shape of the recorded waveform. The classification algorithm achieved up to 100% accuracy on the training data and the validation data.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Bucci, Brian
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairVipperman, Jeffrey Sjsv@pitt.eduJSV
Committee MemberCole, Danieldgcole@pitt.eduDGCOLE
Committee MemberMiller, Markmcmllr@pitt.eduMCMLLR
Date: 30 January 2008
Date Type: Completion
Defense Date: 17 October 2007
Approval Date: 30 January 2008
Submission Date: 3 December 2007
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: MSME - Master of Science in Mechanical Engineering
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: acoustics; impulse noise; military noise; neural networks
Other ID:, etd-12032007-205727
Date Deposited: 10 Nov 2011 20:07
Last Modified: 15 Nov 2016 13:52


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