Pattern Recognition and Artificial

Music genre meta-data is of paramount importance for the organization of music reposito-ries. 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 sym-bolic 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