This paper presents the Jazz Transformer, a generative model that utilizes a
neural sequence model called the Transformer-XL for modeling lead sheets of
Jazz music. Moreover, the model endeavors to incorporate structural events
present in the Weimar Jazz Database (WJazzD) for inducing structures in the
generated 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 of
the generated and real compositions. We therefore go one step further and
conduct a series of computational analysis of the generated compositions from
different perspectives. This includes analyzing the statistics of the pitch
class, grooving, and chord progression, assessing the structureness of the
music with the help of the fitness scape plot, and evaluating the model's
understanding of Jazz music through a MIREX-like continuation prediction task.
Our work presents in an analytical manner why machine-generated music to date
still falls short of the artwork of humanity, and sets some goals for future
work on automatic composition to further pursue.

本文介绍了一种名为 Jazz Transformer 的生成模型，利用名为 Transformer-XL 的神经序列模型来建模爵士乐谱。此外，该模型将 Weimar Jazz 数据库中的结构事件作为诱导生成音乐结构的参考。尽管我们能够将训练损失降低到较低值，但我们的听觉测试显示，生成音乐的平均评分与真实曲目的评分之间存在明显差距。因此，我们会从不同的角度进行一系列计算分析，以进一步证明生成作品与人类艺术作品之间的差距，并为自动创作的未来工作设定一些目标。