Probabilistic modeling of musical chord sequences for music analysis

The aim of this work is to present a model for representing chord sequences using n-grams, an approach from the language recognition field. Previous works show that there exist various means to represent chords, with more or less accuracy. Underlined limitations in [14] are the modeling of rare chords and the representation of chords using an appropriate alphabet. Recent works in the METISS team [14] proposed different labelling schemes and smoothing methods to overcome these limitations. It has been shown that smoothing provides better generalization of the models, yielding better results in unmet situations. In this work we compare two methods from the language recognition field, in different situations using two different datasets and seven different chord notations. Finally, we present two possible applications of n-gram modeling regarding music classification.