Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer

Abstract--User preference music transfer (UPMT) is a new problem in music style transfer that can be applied to many scenarios but remains understudied. Transferring an arbitrary song to fit a user's preferences increases musical diversity and Most music style transfer approaches rely on datadriven methods. In general, however, constructing a large training Figure 1: A demonstration of UPMT: Transferring symbolic input music dataset is challenging because users can rarely provide enough of to new symbolic music that fits a user's preferences based on features their favorite songs. To address this problem, this paper proposes of their favorite music. For example, Marino et al. [17] used prior semantic knowledge in the form of knowledge graphs HERE has been recent growth in research around music style transfer, a technique that transfers the style of to improve image classification performance. Donadello et al. one piece of music to another based on different levels of [18] extracted semantic representations in a knowledge base music representations [1]. Music style transfer is considered to enhance the quality of recommender systems. Despite these important because it increases music variety by reproducing advances, the approaches cannot be directly applied to music, existing music in a creative way.