HOW MUSIC FEATURES AND MUSICAL DATA REPRESENTATIONS AFFECT OBJECTIVE EVALUATION OF MUSIC COMPOSITION: A REVIEW OF THE CSMT DATA CHALLENGE 2020

Tools and methodologies for distinguishing computer-generated melodies from human-composed melodies have a broad range of applications from detecting copyright infringement through the evaluation of generative music systems to facilitating transparent and explainable AI. This paper reviews a data challenge on distinguishing computer-generated melodies from human-composed melodies held in association with the Conference on Sound and Music Technology (CSMT) in 2020. An investigation of the submitted systems and the results are presented first. Besides the structure of the proposed models, the paper investigates two important factors that were identified as contributors to good model performance: the specific music features and the music representation used. Through an analysis of the submissions, important melody-related music features have been identified. Encoding or representation of the music in the context of neural network modes are found noticeably impacting system performance through an experiment where the top-ranked system was re-implemented with different input representations for comparison purposes. Besides demonstrating the feasibility of developing an objective music composition evaluation system, the investigation presented in this paper also reveals some important limitations of current music composition systems opening opportunities for future work in the community.