Paper year
2025
Paper dossier
Symbolic music structure analysis with graph representations and changepoint detection methods
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Citations
0
Authors
0
Topic labels
0
Source readout
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Source slug
unknown
Corpus placement
Controlled edge slice
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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Families present
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8657, citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1731)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=1.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
Under-cited
No materialized row for this family in the resolved run
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Abstract
Music Structure Analysis (MSA), particularly symbolic music boundary detection, is crucial for understanding and creating music, yet segmenting music structure at various hierarchical levels remains an open challenge. In this work, we propose three methods for symbolic music boundary detection: Norm, an adapted feature-based approach, and two novel graph-based algorithms, G-PELT and G-Window. Our graph representations offer a powerful way to encode symbolic music, enabling effective structure analysis without explicit feature extraction. We conducted an extensive ablation study using three public datasets, Schubert Winterreise (SWD), Beethoven Piano Sonatas (BPS) and Essen Folk Dataset, which feature diverse musical forms and instrumentation. This allowed us to compare the methods, optimize their parameters for different music styles, and evaluate performance across low, mid, and high structural levels. Our findings demonstrate that our graph-based approaches are highly effective; for instance, the online and unsupervised G-PELT method achieved an F1-score of 0.5640 with a 1-bar tolerance on the SWD dataset. We further illustrate how algorithm parameters can be adjusted to target specific structural granularities. To promote reproducibility and usability, we have integrated the best-performing methods and their optimal parameters for each structural level into musicaiz, an open-source Python package. We anticipate these methods will benefit various Music Information Retrieval (MIR) tasks, including structure-aware music generation, classification, and key change detection.
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