WikiMT++ is an expanded and refined version of WikiMusicText (WikiMT),
featuring 1010 curated lead sheets in ABC notation. To expand application
scenarios of WikiMT, we add both objective (album, lyrics, video) and
subjective emotion (12 emotion adjectives) and emo\_4q (Russell 4Q) attributes,
enhancing its usability for music information retrieval, conditional music
generation, automatic composition, and emotion classification, etc.
Additionally, CLaMP is implemented to correct the attributes inherited from
WikiMT to reduce errors introduced during original data collection and enhance
the accuracy and completeness of our dataset.

WikiMT++ 是 WikiMusicText (WikiMT) 的扩展和改进版本，包括 1010 个精选的 ABC 符号音乐简谱。为了增加 WikiMT 的应用场景，我们添加了客观属性（专辑、歌词、视频）和主观情感（12 个情感形容词）以及 emo_4q (Russell 4Q) 属性，增强了它在音乐信息检索、条件音乐生成、自动作曲和情感分类等方面的可用性。此外，我们实施了 CLaMP 来纠正从 WikiMT 继承而来的属性，以减少原始数据收集中引入的错误，并提高数据集的准确性和完整性。

WikiMT++ 数据集卡片

WikiMT++ Dataset Card

Commercial adoption of automatic music composition requires the capability of
generating diverse and high-quality music suitable for the desired context
(e.g., music for romantic movies, action games, restaurants, etc.). In this
paper, we introduce combinatorial music generation, a new task to create
varying background music based on given conditions. Combinatorial music
generation creates short samples of music with rich musical metadata, and
combines them to produce a complete music. In addition, we introduce ComMU, the
first symbolic music dataset consisting of short music samples and their
corresponding 12 musical metadata for combinatorial music generation. Notable
properties of ComMU are that (1) dataset is manually constructed by
professional composers with an objective guideline that induces regularity, and
(2) it has 12 musical metadata that embraces composers' intentions. Our results
show that we can generate diverse high-quality music only with metadata, and
that our unique metadata such as track-role and extended chord quality improves
the capacity of the automatic composition. We highly recommend watching our
video before reading the paper (this https URL).

该研究介绍了组合音乐生成和符号音乐数据集，该方法能够仅通过音乐元数据自动组合出多样的高质量音乐，并通过追踪角色和扩展和弦质量等元数据来提高其自动生成音乐的能力。

ComMU: 组合音乐生成数据集

ComMU: Dataset for Combinatorial Music Generation

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。