Automated Music Genre Classification Based on Analyses of Web-Based Documents and Listeners’ Organizational Schemes.
performed by two primary, yet disparate groups: the music industry and consumers of
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.
Advisor:Stephanie W. Haas
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
Date of Publication:05/17/2005