Effectiveness of HMM-Based Retrieval on Large Databases

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dc.contributor.authorJonah Shifrinen_US
dc.contributor.authorWilliam Birminghamen_US
dc.contributor.editorHolger H. Hoosen_US
dc.contributor.editorDavid Bainbridgeen_US
dc.date.accessioned2004-10-21T04:26:21Z
dc.date.available2004-10-21T04:26:21Z
dc.date.issued2003-10-26en_US
dc.description.abstractWe have investigated the performance of a hidden Markov model based QBH retrieval system on a large musical database. The database is synthetic, generated from statistics gleaned from our (smaller) database of musical excerpts from various genres. This paper reports the performance of several variations of our retrieval system against different types of synthetic queries on the large database, where we can control the errors injected into the queries. We note several trends, among the most interesting is that as queries get longer (i.e., more notes) the retrieval performance improves.en_US
dc.format.extent330054 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.isbn0-9746194-0-Xen_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/8
dc.language.isoen_US
dc.publisherJohns Hopkins Universityen_US
dc.subjectIR Systems and Algorithmsen_US
dc.subjectDigital Librariesen_US
dc.titleEffectiveness of HMM-Based Retrieval on Large Databasesen_US
dc.typeArticleen_US
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