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dc.contributor.authorArie Livshinen_US
dc.contributor.authorXavier Rodeten_US
dc.contributor.editorHolger H. Hoosen_US
dc.contributor.editorDavid Bainbridgeen_US
dc.date.accessioned2004-10-21T04:26:37Z
dc.date.available2004-10-21T04:26:37Z
dc.date.issued2003-10-26en_US
dc.identifier.isbn0-9746194-0-Xen_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/42
dc.description.abstractIn numerous articles (Martin and Kim, 1998; Fraser and Fujinaga, 1999; and many others) sound classification algorithms are evaluated using "self classification" - the learning and test groups are randomly selected out of the same sound database. We will show that "self classification" is not necessarily a good statistic for the ability of a classification algorithm to learn, generalize or classify well. We introduce the alternative "Minus-1 DB" evaluation method and demonstrate that it does not have the shortcomings of "self classification".en_US
dc.format.extent129194 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherJohns Hopkins Universityen_US
dc.subjectIR Systems and Algorithmsen_US
dc.subjectMusic Analysisen_US
dc.titleThe Importance of Cross Database Evaluation in Sound Classificationen_US
dc.typeArticleen_US


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