  Research on style transfer and domain translation has clearly demonstrated
the ability of deep learning-based algorithms to manipulate images in terms of
artistic style. More recently, several attempts have been made to extend such
approaches to music (both symbolic and audio) in order to enable transforming
musical style in a similar manner. In this study, we focus on symbolic music
with the goal of altering the 'style' of a piece while keeping its original
'content'. As opposed to the current methods, which are inherently restricted
to be unsupervised due to the lack of 'aligned' data (i.e. the same musical
piece played in multiple styles), we develop the first fully supervised
algorithm for this task. At the core of our approach lies a synthetic data
generation scheme which allows us to produce virtually unlimited amounts of
aligned data, and hence avoid the above issue. In view of this data generation
scheme, we propose an encoder-decoder model for translating symbolic music
accompaniments between a number of different styles. Our experiments show that
our models, although trained entirely on synthetic data, are capable of
producing musically meaningful accompaniments even for real (non-synthetic)
MIDI recordings.
