Genre classification using chords and stochastic language models
Carlos Pérez-Sancho, David Rizo and José Iñesta
Number 2 & 3,
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 naïve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered trying to investigate how far can we reach using harmonic information under these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.