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dc.contributor.authorCabell, Randolph H.en_US
dc.date.accessioned2014-03-14T21:50:24Z
dc.date.available2014-03-14T21:50:24Z
dc.date.issued1989-02-15en_US
dc.identifier.otheretd-11212012-040018en_US
dc.identifier.urihttp://hdl.handle.net/10919/45932
dc.description.abstract

This work describes the design of an intelligent recognition system used to distinguish noise signatures of five different acoustic sources. The system uses pattern recognition techniques to identify the information obtained from a single microphone. A training phase is used in which the system learns to distinguish the sources and automatically selects features for optimal performance. Results were obtained by training the system to distinguish jet planes, propeller planes, a helicopter, train, and wind turbine from one another, then presenting similar sources to the system and recording the number of errors. These results indicate the system can successfully identify the trained sources based on acoustic information. Classification errors highlight the impact of the training sources on the system's ability to recognize different sources.

en_US
dc.format.mediumBTDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartLD5655.V855_1989.C323.pdfen_US
dc.subjectAirplanesen_US
dc.subject.lccLD5655.V855 1989.C323en_US
dc.titleThe automatic identification of aerospace acoustic sourcesen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineMechanical Engineeringen_US
dc.contributor.committeechairFuller, Christopher R.en_US
dc.contributor.committeememberO'Brien, Walter F. Jr.en_US
dc.contributor.committeememberWicks, Alfred L.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-11212012-040018/en_US
dc.date.sdate2012-11-21en_US
dc.date.rdate2012-11-21
dc.date.adate2012-11-21en_US


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