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Learning the meaning of music

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dc.contributor.advisor Barry L. Vercoe. en_US
dc.contributor.author Whitman, Brian A. (Brian Alexander) en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences en_US
dc.date.accessioned 2007-12-07T19:20:39Z
dc.date.available 2007-12-07T19:20:39Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://dspace.mit.edu/handle/1721.1/32500 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/32500
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005. en_US
dc.description Includes bibliographical references (p. 99-104). en_US
dc.description.abstract Expression as complex and personal as music is not adequately represented by the signal alone. We define and model meaning in music as the mapping between the acoustic signal and its contextual interpretation - the 'community metadata' based on popularity, description and personal reaction, collected from reviews, usage, and discussion. In this thesis we present a framework for capturing community metadata from free text sources, audio representations general enough to work across domains of music, and a machine learning framework for learning the relationship between the music signals and the contextual reaction iteratively at a large scale. Our work is evaluated and applied as semantic basis functions - meaning classifiers that are used to maximize semantic content in a perceptual signal. This process improves upon statistical methods of rank reduction as it aims to model a community's reaction to perception instead of relationships found in the signal alone. We show increased accuracy of common music retrieval tasks with audio projected through semantic basis functions. We also evaluate our models in a 'query-by-description' task for music, where we predict description and community interpretation of audio. These unbiased learning approaches show superior accuracy in music and multimedia intelligence tasks such as similarity, classification and recommendation. en_US
dc.description.statementofresponsibility by Brian A. Whitman. en_US
dc.format.extent 104 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/32500 en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Architecture. Program In Media Arts and Sciences en_US
dc.title Learning the meaning of music en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences en_US
dc.identifier.oclc 61896668 en_US


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