mlsanityData to 7 Oct 2026

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

arXiv 2403.07995 ↗First posted 12 Mar 2024cs.SD cs.LG cs.SCcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 34 of its 78 references that are in the map. First posted on arXiv 12 Mar 2024.

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34 of 78 references.

References · 34

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22 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.