Music Style Transfer Issues: A Position Paper.
Shuqi Dai, Zheng Zhang, Gus Xia · arXiv (Cornell University) · 2018
Led by the success of neural transfer on visual arts, there has been a rising trend very recently in the effort of 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 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 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 representation at once by directly adopting the method of image transfer, leads to poor results. Thus, we see a vital necessity to re-define transfer more precisely and scientifically based on the uniqueness of representation, as well as to connect different aspects of transfer with existing well-established sub-fields of computer studies. Otherwise, an accumulated upcoming literature (all named after transfer) will lead to a great confusion of the underlying problems as well as negligence of the treasures in computer before the age of deep learning. In addition, we discuss the current limitations of modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.