A hybrid approach to supervised machine learning for algorithmic melody
  composition
  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.
