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Automated Music Genre Classification Based on Analyses of Web-Based Documents and Listeners’ Organizational Schemes.

by Hannah, William P.

Abstract (Summary)
This paper describes a two-part study attempting to correlate music genre assignments

performed by two primary, yet disparate groups: the music industry and consumers of

popular music.

An online survey was conducted, aimed at evaluating the latter group's perception of music genre. The sample of the survey consisted of 15 UNC-CH students affiliated with the music department. Concurrently, a series of genre classification experiments were conducted on several corpora of music reviews harvested from authoritative, online review websites. Results of the survey were subsequently triangulated with a portion of the music review corpora in a final genre classification experiment.

The genre classification experiments were quite successful, yielding a maximum of 91% accuracy using web-based data alone. The effect of weighting schemes and procedural modifications on experimental accuracy rates are discussed, as are qualitative evaluations of participants' responses to the survey.

Bibliographical Information:

Advisor:Stephanie W. Haas

School:University of North Carolina at Chapel Hill

School Location:USA - North Carolina

Source Type:Master's Thesis

Keywords:automation of library processes – classification music libraries and collections information retrieval genre user needs evaluation indexing automatic

ISBN:

Date of Publication:05/17/2005

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