A hybrid approach to supervised machine learning for algorithmic melody composition
Artificial Intelligence
2016-12-30 v1
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.
Cite
@article{arxiv.1612.09212,
title = {A hybrid approach to supervised machine learning for algorithmic melody composition},
author = {Rouven Bauer},
journal= {arXiv preprint arXiv:1612.09212},
year = {2016}
}