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 see a vital necessity to re-define music style transfer more precisely and scientifically based on the uniqueness of music representation, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. Otherwise, an accumulated upcoming literature (all named after music style transfer) will lead to a great confusion of the underlying problems as well as negligence of the treasures in computer music before the age of deep learning. 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.
A successful performance style transfer would allow us to transfer Louis Armstrong's interpretation of Summertime to the one of Miles Davis. It is closely related to expressive performance rendering , which studies how to convert static scores into human-like expressive performances by different computational models. @cite_40 @cite_29 @cite_4 The difference is that performance style transfer requires a disentanglement of control (style) and score information (content) and implies that there is room to create new musical expression through the combination of different controls.
@cite_40 @cite_29 @cite_4
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We present a survey of research into automated and semiautomated computer systems for expressive performance of music. We will examine the motivation for such systems and then examine the majority of the systems developed over the last 25 years. To highlight some of the possible future directions for new research, the review uses primary terms of reference based on four elements: testing status, expressive representation, polyphonic ability, and performance creativity. This contribution gives an overview of the state of the art in the field of computational modeling of expressive music performance. The notion of predictive computational model is briefly discussed, and a number of quantitative models of various aspects of expressive performance are briefly reviewed. Four selected computational models are reviewed in some detail. Their basic principles and assumptions are explained and, wherever possible, empirical evaluations of the models on real performance data are reported. In addition to these models, which focus on general, common principles of performance, currently ongoing research on the formal characterisation of differences in individual performance style are briefly presented.