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dc.contributor.authorRoger Dannenbergen_US
dc.contributor.authorWilliam Birminghamen_US
dc.contributor.authorGeorge Tzanetakisen_US
dc.contributor.authorColin Meeken_US
dc.contributor.authorNing Huen_US
dc.contributor.authorBryan Pardoen_US
dc.contributor.editorHolger H. Hoosen_US
dc.contributor.editorDavid Bainbridgeen_US
dc.date.accessioned2004-10-21T04:26:22Z
dc.date.available2004-10-21T04:26:22Z
dc.date.issued2003-10-26en_US
dc.identifier.isbn0-9746194-0-Xen_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/9
dc.description.abstractEvaluating music information retrieval systems is acknowledged to be a difficult problem. We have created a database and a software testbed for the systematic evaluation of various query-by-humming (QBH) search systems. As might be expected, different queries and different databases lead to wide variations in observed search precision. "Natural" queries from two sources led to significantly lower performance than that typically reported in the QBH literature. These results point out the importance of careful measurement and objective comparisons to study retrieval algorithms. We compare string-matching, contour-matching, and hidden Markov model search algorithms in this study. An examination of scaling trends is encouraging: precision falls off very slowly as the database size increases. This trend is simple to compute and could be useful to predict performance on larger databases.en_US
dc.format.extent146236 bytes
dc.format.mimetypeapplication/pdf
dc.languageenen_US
dc.language.isoen_US
dc.publisherJohns Hopkins Universityen_US
dc.subjectIR Systems and Algorithmsen_US
dc.subjectDigital Librariesen_US
dc.titleThe MUSART Testbed for Query-By-Humming Evaluationen_US
dc.typearticleen_US


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