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.

Abstract. In this paper we present an application of language modeling using n-grams to model the style of different composers. For this, we repeated the experiments performed in previous works by other authors using a corpus of 5 composers from the Baroque and Classical periods. In these experiments we found some signs that the results could be influenced by external factors other than the composers' styles, such as the heterogeneity in the musical forms selected for the corpus. In order to assess the validity of the modeling techniques to capture the own personal style of the composers, a new experiment was performed with a corpus of fugues from Bach and Shostakovich. All these experiments show that language modeling is a suitable tool for modeling musical style, even when the styles of the different datasets are affected by several factors.

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Abstract. 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. This work brings to symbolic music recognition some technologies, like the stochastic language models, already successfully applied to text categorization. In this work we model chord progressions and melodies 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. Also a combination of the different techniques as an ensemble of classifiers is proposed. Some genres and sub-genres among popular, jazz, and academic music have been considered. The results show that the ensemble is a good trade-off approach able to perform well without the risk of choosing the wrong classifier.