We present preliminary work on automatic human-readable melody characterization. In order to obtain such a characterization, we (1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files, (2) apply a rule induction algorithm to obtain a set of (crisp) classification rules for melody track identification, and (3) automatically transform the crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership functions for the rule attributes. Some results are presented and discussed.

This work is supported by the projects: GV06/166 and CICyT TIN2006–14932–C02, partially supported by EU ERDF and the Pascal Network of Excellence.