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dc.contributor.authorVarma, S.en_US
dc.contributor.authorBaras, John S.en_US
dc.contributor.authorShamma, S. A.en_US
dc.date.accessioned2007-05-23T10:11:48Z
dc.date.available2007-05-23T10:11:48Z
dc.date.issued2001en_US
dc.identifier.urihttp://hdl.handle.net/1903/6257
dc.description.abstractWe report on a novel method of acoustic wear analysis using spectral classification based on a model of mammalian audition. This approach uses biologically-inspired pre-processing filters that give a multi- resolution representation of the sound timbre. A Tree Structured Vector Quantizer (TSVQ) is used to create a classification tree based on the wear labels. We have obtained encouraging results for tools of different diameters, cutting different materials. Trees trained on one kind of data seem to generalize well to new data sets. <P><Center><I> The research and scientific content in this material have been accepted for the Nonlinear Signal and Image Processing (NSIP) 2001 Conference at Baltimore, MD, 2001 </I></Center>en_US
dc.format.extent282355 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2001-11en_US
dc.relation.ispartofseriesCAAR; TR 2001-1en_US
dc.subjectSensor-Actuator Networksen_US
dc.titleBiologically-Inspired Acoustic Wear Analysisen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US
dc.contributor.departmentCAARen_US


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