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dc.contributor.authorJana Egginken_US
dc.contributor.authorGuy J. Brownen_US
dc.contributor.editorHolger H. Hoosen_US
dc.contributor.editorDavid Bainbridgeen_US
dc.date.accessioned2004-10-21T04:26:27Z
dc.date.available2004-10-21T04:26:27Z
dc.date.issued2003-10-26en_US
dc.identifier.isbn0-9746194-0-Xen_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/18
dc.description.abstractA system for musical instrument recognition based on a Gaussian Mixture Model (GMM) classifier is introduced. To enable instrument recognition when more than one sound is present at the same time, ideas from missing feature theory are incorporated. Specifically, frequency regions that are dominated by energy from an interfering tone are marked as unreliable and excluded from the classification process. The approach has been evaluated on clean and noisy monophonic recordings, and on combinations of two instrument sounds. These included random chords made from two isolated notes and combinations of two realistic phrases taken from commercially available compact discs. Classification results were generally good, not only when the decision between reliable and unreliable features was based on the knowledge of the clean signal, but also when it was solely based on the pitch and harmonic overtone series of the interfering sound.en_US
dc.format.extent82915 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherJohns Hopkins Universityen_US
dc.subjectAudioen_US
dc.subjectMetadataen_US
dc.titleApplication of missing feature theory to the recognition of musical instruments in polyphonic audioen_US
dc.typeArticleen_US


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