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Learning the Meaning of Music

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Learning the Meaning of Music
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  Learning the Meaning of Music by  Brian A. Whitman B.S. Computer Science, Worcester Polytechnic Institute, 1999B.S. English Literature, Worcester Polytechnic Institute, 1999M.S. Computer Science, Columbia University, 2001Submitted to the Program in Media Arts and Sciences,School of Architecture and Planning,in partial fulfillment of the requirements for the degree of Doctor of Philosophy at theMASSACHUSETTS INSTITUTE OF TECHNOLOGYJune, 2005c  Massachusetts Institute of Technology 2005. All rights reserved.Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Program in Media Arts and SciencesApril 29, 2005Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Barry L. VercoeProfessor of Media Arts and SciencesThesis SupervisorAccepted by. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Andrew B. LippmanGraduate Officer, Departmental Committee on Graduate Students  2  Learning the Meaning of Music by Brian A. Whitman Submitted to the Program in Media Arts and Sciences,School of Architecture and Planning, on April 29, 2005,in partial fulfillment of the requirementsfor the degree of Doctor of Philosophy  Abstract Expression as complex and personal as music is not adequately represented by the sig-nal alone. We define and model  meaning   in music as the mapping between the acousticsignal and its contextual interpretation – the ‘community metadata’ based on popular-ity, descriptionandpersonalreaction, collectedfromreviews, usage, anddiscussion. Inthis thesis we present a framework for capturing community metadata from free textsources, audio representations general enough to work across domains of music, anda machine learning framework for learning the relationship between the music signalsand the contextual reaction iteratively at a large scale.Our work is evaluated and applied as  semantic basis functions   – meaning classifiers thatare used to maximize semantic content in a perceptual signal. This process improvesupon statistical methods of rank reduction as it aims to model a community’s reactionto perception instead of relationships found in the signal alone. We show increasedaccuracy of common music retrieval tasks with audio projected through semantic ba-sis functions. We also evaluate our models in a ‘query-by-description’ task for music,where we predict description and community interpretation of audio. These unbi-ased learning approaches show superior accuracy inmusic and multimediaintelligencetasks such as similarity, classification and recommendation.Thesis supervisor: Barry L. Vercoe, D.M.A.Title: Professor of Media Arts and Sciences Abstract 3  4  Thesis Committee Thesis supervisor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Barry L. VercoeProfessor of Media Arts and SciencesMassachusetts Institute of Technology Thesis reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Daniel P.W. EllisAssistant Professor of Electrical EngineeringColumbia University Thesis reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Deb K. Roy Associate Professor of Media Arts and SciencesMassachusetts Institute of Technology  Thesis Committee 5
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