Classi cation in Music : A Computational Model forParadigmatic

We present a computational model for the paradigmatic analysis of musical pieces, which is the classiication of musical segments into similarity-based categories. The model requires the analyst to make explicit choices for the characteristics by which the musical segments are described. The classiication of the segments into categories is determined by these characteristics and is performed by a self-organizing neural network algorithm. In this way, traditional problems associated with paradigmatic analysis, namely lack of consistency and objectivity, can be avoided. Moreover, the model extends the analytical technique by providing diierent levels of classiication, prototypes for each class, and by showing relations between classes.