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.

翻译：音乐结构建模对于生成符号音乐作品的人工智能系统至关重要，却极具挑战性。本文献综述深入剖析了从符号方法到基础性与变革性深度学习方法（这些方法利用计算与数据的力量，跨越多种训练范式）中，融入连贯结构的技术演进历程。在后续阶段，我们回顾了一种新兴技术——称之为"子任务分解"，即将音乐生成分解为独立的高层结构规划与内容创作阶段。此类系统通过提取旋律骨架或结构模板来引导生成过程，融入了某种形式的音乐知识或神经符号方法。在回顾的三个时期中，捕捉动机与重复的进展显而易见，然而，模仿人类作曲家风格在长篇作品中细腻发展主题的建模仍然困难重重。我们概述了未来关键方向，以实现融合所有时期方法所产生的协同效应。