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dc.contributor.authorShan, Man-Kwanen_US
dc.contributor.authorChiang, Meng-Fenen_US
dc.contributor.authorKuo, Fang-Feien_US
dc.date.accessioned2014-12-08T15:10:58Z-
dc.date.available2014-12-08T15:10:58Z-
dc.date.issued2008-09-01en_US
dc.identifier.issn1380-7501en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s11042-008-0201-8en_US
dc.identifier.urihttp://hdl.handle.net/11536/8400-
dc.description.abstractTraditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested. However, the need exists for the development of category search for the retrieval of a specific category of music objects which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic. Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed relevance feedback mechanism.en_US
dc.language.isoen_USen_US
dc.subjectcategory searchen_US
dc.subjectmusic retrievalen_US
dc.subjectrelevance feedbacken_US
dc.subjectsemantic concept learningen_US
dc.titleRelevance feedback for category search in music retrieval based on semantic concept learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11042-008-0201-8en_US
dc.identifier.journalMULTIMEDIA TOOLS AND APPLICATIONSen_US
dc.citation.volume39en_US
dc.citation.issue2en_US
dc.citation.spage243en_US
dc.citation.epage262en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000257381400006-
dc.citation.woscount3-
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