Genre classification using chords and stochastic language models

Music genre meta-data is of paramount importance for the organization of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorization. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and Naive Bayes classifiers in order to model how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered and the results at different levels of the genre hierarchy for the techniques employed are presented and discussed.