A hybrid approach to supervised machine learning for algorithmic melody composition
This work addresses the problem of generating stylistically similar monophonic melodies for music composition applications, representing an incremental improvement in the field.
The authors tackled algorithmic melody composition by developing a hybrid method combining parametric Markov models with a contour concept, which significantly improved results over a pure Markov model according to an online listening test.
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.