  Automatic melody generation for pop music has been a long-time aspiration for
both AI researchers and musicians. However, learning to generate euphonious
melody has turned out to be highly challenging due to a number of factors.
Representation of multivariate property of notes has been one of the primary
challenges. It is also difficult to remain in the permissible spectrum of
musical variety, outside of which would be perceived as a plain random play
without auditory pleasantness. Observing the conventional structure of pop
music poses further challenges. In this paper, we propose to represent each
note and its properties as a unique `word,' thus lessening the prospect of
misalignments between the properties, as well as reducing the complexity of
learning. We also enforce regularization policies on the range of notes, thus
encouraging the generated melody to stay close to what humans would find easy
to follow. Furthermore, we generate melody conditioned on song part
information, thus replicating the overall structure of a full song.
Experimental results demonstrate that our model can generate auditorily
pleasant songs that are more indistinguishable from human-written ones than
previous models.
