  Led by the success of neural style transfer on visual arts, there has been a
rising trend very recently in the effort of music style transfer. However,
"music style" is not yet a well-defined concept from a scientific point of
view. The difficulty lies in the intrinsic multi-level and multi-modal
character of music representation (which is very different from image
representation). As a result, depending on their interpretation of "music
style", current studies under the category of "music style transfer", are
actually solving completely different problems that belong to a variety of
sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims
at dealing with all levels of music representation at once by directly adopting
the method of image style transfer, leads to poor results. Thus, we vitally
propose a more scientifically-viable definition of music style transfer by
breaking it down into precise concepts of timbre style transfer, performance
style transfer and composition style transfer, as well as to connect different
aspects of music style transfer with existing well-established sub-fields of
computer music studies. In addition, we discuss the current limitations of
music style modeling and its future directions by drawing spirit from some deep
generative models, especially the ones using unsupervised learning and
disentanglement techniques.
