Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Shapiro, Ilana; Huang, Ruanqianqian (Lisa); Novack, Zachary; Wang, Cheng-i; Dong, Hao-Wen; Berg-Kirkpatrick, Taylor; Dubnov, Shlomo; Lerner, Sorin

doi:10.24963/ijcai.2025/1128

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Ilana Shapiro, Ruanqianqian (Lisa) Huang, Zachary Novack, Cheng-i Wang, Hao-Wen Dong, Taylor Berg-Kirkpatrick, Shlomo Dubnov, Sorin Lerner

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI, Arts & Creativity. Pages 10153-10161.

https://doi.org/10.24963/ijcai.2025/1128

Keywords

Theory and philosophy of arts and creativity in AI systems: Computational paradigms, architectures and models for creativity
Application domains: Music and sound

Abstract

Western music is an innately hierarchical system of interacting levels of structure, from fine-grained melody to high-level form. In order to analyze music compositions holistically and at multiple granularities, we propose a unified, hierarchical meta-representation of musical structure called the structural temporal graph (STG). For a single piece, the STG is a data structure that defines a hierarchy of progressively finer structural musical features and the temporal relationships between them. We use the STG to enable a novel approach for deriving a representative structural summary of a music corpus, which we formalize as a dually NP-hard combinatorial optimization problem. Our approach first applies simulated annealing to develop a measure of structural distance between two music pieces rooted in graph isomorphism. Our approach then combines the formal guarantees of SMT solvers with nested simulated annealing over structural distances to produce a structurally sound, representative centroid STG for an entire corpus of STGs from individual pieces. To evaluate our approach, we conduct experiments verifying that structural distance accurately differentiates between music pieces, and that derived centroids accurately structurally characterize their corpora.

Ilana Shapiro, Ruanqianqian (Lisa) Huang, Zachary Novack, Cheng-i Wang, Hao-Wen Dong, Taylor Berg-Kirkpatrick, Shlomo Dubnov, and Sorin Lerner. 2025. Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10153–10161. https://doi.org/10.24963/ijcai.2025/1128

@inproceedings{ijcai2025p1128,
  title     = {Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora},
  author    = {Shapiro, Ilana and Huang, Ruanqianqian (Lisa) and Novack, Zachary and Wang, Cheng-i and Dong, Hao-Wen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo and Lerner, Sorin},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10153--10161},
  year      = {2025},
  month     = {8},
  note      = {AI, Arts & Creativity},
  doi       = {10.24963/ijcai.2025/1128},
  url       = {https://doi.org/10.24963/ijcai.2025/1128},
}

TY  - CPAPER
TI  - Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora
AU  - Shapiro, Ilana
AU  - Huang, Ruanqianqian (Lisa)
AU  - Novack, Zachary
AU  - Wang, Cheng-i
AU  - Dong, Hao-Wen
AU  - Berg-Kirkpatrick, Taylor
AU  - Dubnov, Shlomo
AU  - Lerner, Sorin
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10153
EP  - 10161
DO  - 10.24963/ijcai.2025/1128
UR  - https://doi.org/10.24963/ijcai.2025/1128
ER  -