The Importance of Cross Database Evaluation in Sound Classification
dc.contributor.author | Arie Livshin | en_US |
dc.contributor.author | Xavier Rodet | en_US |
dc.contributor.editor | Holger H. Hoos | en_US |
dc.contributor.editor | David Bainbridge | en_US |
dc.date.accessioned | 2004-10-21T04:26:37Z | |
dc.date.available | 2004-10-21T04:26:37Z | |
dc.date.issued | 2003-10-26 | en_US |
dc.description.abstract | In 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.extent | 129194 bytes | |
dc.format.mimetype | application/pdf | |
dc.identifier.isbn | 0-9746194-0-X | en_US |
dc.identifier.uri | http://jhir.library.jhu.edu/handle/1774.2/42 | |
dc.language.iso | en_US | |
dc.publisher | Johns Hopkins University | en_US |
dc.subject | IR Systems and Algorithms | en_US |
dc.subject | Music Analysis | en_US |
dc.title | The Importance of Cross Database Evaluation in Sound Classification | en_US |
dc.type | Article | en_US |
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