Graph Neural Network-Based Musical Structure Modeling and Singing–Melody Relationship Analysis
Keywords:
graph neural networks; music information retrieval; music structure analysis; singing voice processing; melody alignment; sociotechnical systemsAbstract
The computational analysis of musical structure and the relationship between singing performance and notated melody has historically relied on sequential or grid-like representations that do not fully capture the relational dependencies among musical events. Graph neural networks offer a compelling alternative by modeling notes, beats, measures, segments, and vocal contours as interacting nodes in a graph, with edges encoding temporal continuity, harmonic similarity, repetition, and score-to-performance alignment. This paper presents an interdisciplinary examination of graph neural network-based musical structure modeling and singing-melody relationship analysis from a systems perspective. It discusses how graph representations can integrate audio features, symbolic score information, and singing voice characteristics within a unified relational framework. The paper addresses architectural trade-offs involving message passing schemes, node granularity, heterogeneous edge types, and scalability across large and culturally diverse corpora. Beyond algorithmic considerations, the work analyzes infrastructure deployment challenges, data governance, robustness under noisy field recordings, fairness across musical traditions, and sustainability of large-scale model training. The discussion highlights the importance of treating these systems not as isolated classifiers but as socio-technical infrastructures embedded in creative, archival, and educational contexts. The paper concludes with forward-looking perspectives on transparent evaluation, community-centered dataset governance, and energy-aware deployment for music structure and singing analysis systems.
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