论文标题
前线的爵士变压器：通过定量措施探索AI组成的音乐的缺点
The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures
论文作者
论文摘要
本文介绍了爵士变压器，这是一种生成模型，该模型利用称为Transformer-XL的神经序列模型来建模爵士音乐的铅片。此外，模型努力纳入魏玛爵士数据库（Wjazzd）中存在的结构事件，以诱导生成的音乐中的结构。尽管我们能够将训练损失减少到低价值，但我们的听力测试表明，生成的和真实成分的平均评分之间存在明显的差距。因此，我们进一步走了一步，并从不同的角度对生成的组成进行了一系列计算分析。这包括分析音调类别，凹槽和和弦进展的统计数据，借助健身乐谱图评估音乐的结构性，并通过类似Mirex的持续预测任务评估模型对爵士音乐的理解。我们的作品以分析方式展示了为什么迄今为止机器生成的音乐仍然没有人类艺术品，并为将来的自动构图上的工作设定了一些目标，以进一步追求。
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.
