Please use this identifier to cite or link to this item: https://doi.org/10.1145/1508850.1508856
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dc.titleA novel framework for efficient automated singer identification in large music databases
dc.contributor.authorShen, J.
dc.contributor.authorShepherd, J.
dc.contributor.authorCui, B.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2013-07-04T07:33:28Z
dc.date.available2013-07-04T07:33:28Z
dc.date.issued2009
dc.identifier.citationShen, J., Shepherd, J., Cui, B., Tan, K.-L. (2009). A novel framework for efficient automated singer identification in large music databases. ACM Transactions on Information Systems 27 (3). ScholarBank@NUS Repository. https://doi.org/10.1145/1508850.1508856
dc.identifier.issn10468188
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39079
dc.description.abstractOver the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for efficient automated singer recognition. HSI uses multiple low-level features extracted from both vocal and nonvocal music segments to enhance the identification process; it achieves this via a hybrid architecture that builds profiles of individual singer characteristics based on statistical mixture models. An extensive experimental study on a large music database demonstrates the superiority of our method over state-of-the-art approaches in terms of effectiveness, efficiency, scalability, and robustness. © 2009 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1508850.1508856
dc.sourceScopus
dc.subjectClassification
dc.subjectEM algorithm
dc.subjectEvaluation
dc.subjectGaussian mixture models
dc.subjectMusic retrieval
dc.subjectSinger identification
dc.subjectStatistical modeling
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1508850.1508856
dc.description.sourcetitleACM Transactions on Information Systems
dc.description.volume27
dc.description.issue3
dc.description.codenATISE
dc.identifier.isiut000267279300006
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