1. P.R. Illescas, D. Rizo, J.M. Iņesta, R. Ramirez.

, vol. 4th Int.Workshop on Music and Machine Learning, pp. . (2011)

Automatic musical analysis has been approached from different perspectives: grammars, expert systems, probabilistic models, and model matching have been proposed for implementing tonal analysis. In this work we focus on automatic melodic analysis. One question that arises when building a melodic analysis system using a-priori music theory is whether it is possible to automatically extract analysis rules from examples, and how similar are those learnt rules compared to music theory rules. This work investigates this question, i.e. given a dataset of analyzed melodies our objective is to automatically learn analysis rules and to compare them with music theory rules.