THIS STUDY EXPLORES WAYS OF MODELING the compositional processes involved in common-practice rhythm (as represented by European classical music and folk music). Six probabilistic models of rhythm were evaluated using the method of cross-entropy: according to this method, the best model is the one that assigns the highest probability to the data. Two corpora were used: a corpus of European folk songs (the Essen Folksong Collection) and a corpus of Mozart and Haydn string quartets. The model achieving lowest cross-entropy was the First-Order Metrical Duration Model, which chooses a metrical position for each note conditional on the position of the previous note. Second best was the Hierarchical Position Model, which decides at each beat whether or not to generate a note there, conditional on the note status of neighboring strong beats (i.e., whether or not they contain notes).When complexity (number of parameters) is also considered, it is argued that the Hierarchical Position Model is preferable overall.

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