Elements of information theory
Elements of information theory
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Statistical methods for speech recognition
Statistical methods for speech recognition
Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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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.