Modelling musical structure is vital yet challenging for artificial
intelligence systems that generate symbolic music compositions. This literature
review dissects the evolution of techniques for incorporating coherent
structure, from symbolic approaches to foundational and transformative deep
learning methods that harness the power of computation and data across a wide
variety of training paradigms. In the later stages, we review an emerging
technique which we refer to as "sub-task decomposition" that involves
decomposing music generation into separate high-level structural planning and
content creation stages. Such systems incorporate some form of musical
knowledge or neuro-symbolic methods by extracting melodic skeletons or
structural templates to guide the generation. Progress is evident in capturing
motifs and repetitions across all three eras reviewed, yet modelling the
nuanced development of themes across extended compositions in the style of
human composers remains difficult. We outline several key future directions to
realize the synergistic benefits of combining approaches from all eras
examined.

模拟音乐结构在生成符号音乐作品的人工智能系统中至关重要但具有挑战性。本文概述了整合连贯结构的技术的演变，从符号方法到基础和转换的深度学习方法，利用计算和数据在各种训练范式中的强大能力。最新阶段，我们评述了一种新兴技术，称为 “子任务分解”，它涉及将音乐生成分解为独立的高级结构规划和内容创作阶段。这些系统通过提取旋律的骨架或结构模板来引导生成过程，从而纳入某种形式的音乐知识或神经符号方法。从审查的三个时期来看，已经取得了在主题和重复方面的进展，但在模拟人类作曲家风格下的扩展音乐作品中细微主题的发展仍然很困难。我们概述了几个关键的未来方向，以实现结合所有考察时期方法的协同效益。

符号音乐生成中的结构建模：图案、短语与更多

Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic  Music Generation

In complex inferential tasks like question answering, machine learning models
must confront two challenges: the need to implement a compositional reasoning
process, and, in many applications, the need for this reasoning process to be
interpretable to assist users in both development and prediction. Existing
models designed to produce interpretable traces of their decision-making
process typically require these traces to be supervised at training time. In
this paper, we present a novel neural modular approach that performs
compositional reasoning by automatically inducing a desired sub-task
decomposition without relying on strong supervision. Our model allows linking
different reasoning tasks though shared modules that handle common routines
across tasks. Experiments show that the model is more interpretable to human
evaluators compared to other state-of-the-art models: users can better
understand the model's underlying reasoning procedure and predict when it will
succeed or fail based on observing its intermediate outputs.

本研究提出一种新颖的神经模块化方法来实现基于组合推理的问题回答，该方法不需要强监督，能自动诱导期望的子任务分解并通过共享模块链接不同的推理任务，实验表明该模型比当前先进模型更易于人类评估者解释以及预测其中间结果的成功或失败。