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This paper presents the Jazz Transformer, a generative model that utilizes aneural sequence model called the Transformer-XL for modeling lead sheets ofJazz music. Moreover, the model endeavors to incorporate structural eventspresent in the Weimar Jazz Database (WJazzD) for inducing structures in thegenerated music. While we are able to reduce the training loss to a low value,our listening test suggests however a clear gap between the average ratings ofthe generated and real compositions. We therefore go one step further andconduct a series of computational analysis of the generated compositions fromdifferent perspectives. This includes analyzing the statistics of the pitchclass, grooving, and chord progression, assessing the structureness of themusic with the help of the fitness scape plot, and evaluating the model'sunderstanding of Jazz music through a MIREX-like continuation prediction task.Our work presents in an analytical manner why machine-generated music to datestill falls short of the artwork of humanity, and sets some goals for futurework on automatic composition to further pursue.

The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures