Uma abordagem híbrida de aprendizado supervisionado com Machine Learning para composição de melodias de forma algorítmica

A hybrid approach to supervised machine learning for algorithmic melody composition

Abstract: In this work we present an algorithm for composing monophonic melodies similar in style to those of a given, phrase annotated, sample of melodies. For implementation, a hybrid approach incorporating parametric Markov models of higher order and a contour concept of phrases is used. This work is based on the master thesis of Thayabaran Kathiresan (2015). An online listening test conducted shows that enhancing a pure Markov model with musically relevant context, like count and planed melody contour, improves the result significantly.

Conclusions: Even though Markov models alone are seen as no proper method for algorithmic composition, we successfully showed that when combined with further methods they can yield much better results in terms of being closer to human composed melodies. This can be seen when comparing our results with the ones of Kathiresan [Kat15], whose basic algorithm solely relies on Markov models. Apart from the previous works, our algorithm outperforms a random guessing baseline, meaning that humans are not able to clearly distinguish its compositions from humans anymore.