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dc.contributor.authorLevy, Mark
dc.description.abstractThis thesis investigates the use of latent semantic models for annotation and retrieval from collections of musical audio tracks. In particular latent semantic analysis (LSA) and aspect models (or probabilistic latent semantic analysis, pLSA) are used to index words in descriptions of music drawn from hundreds of thousands of social tags. A new discrete audio feature representation is introduced to encode musical characteristics of automatically-identified regions of interest within each track, using a vocabulary of audio muswords. Finally a joint aspect model is developed that can learn from both tagged and untagged tracks by indexing both conventional words and muswords. This model is used as the basis of a music search system that supports query by example and by keyword, and of a simple probabilistic machine annotation system. The models are evaluated by their performance in a variety of realistic retrieval and annotation tasks, motivated by applications including playlist generation, internet radio streaming, music recommendation and catalogue searchen_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.publisherQueen Mary University of London
dc.subjectCrowd psychology
dc.subjectPerforming Artsen_US
dc.titleRetrieval and Annotation of Music Using Latent Semantic Modelsen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author

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  • Theses [3184]
    Theses Awarded by Queen Mary University of London

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