AnalysisGNN: Unified Music Analysis with Graph Neural Networks

• URL: http://arxiv.org/abs/2509.06654v1
• Date: Mon, 08 Sep 2025 13:11:54 GMT
• Title: AnalysisGNN: Unified Music Analysis with Graph Neural Networks
• Authors: Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth, Gerhard Widmer,
• Abstract summary: We introduce AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss.<n>We also integrate a Non-Chord-Tone prediction module, which identifies and excludes passing and non-functional notes from all tasks.
• Score: 8.180530393436326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have seen a boom in computational approaches to music analysis, yet each one is typically tailored to a specific analytical domain. In this work, we introduce AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss and logit fusion between task-specific classifiers to integrate heterogeneously annotated symbolic datasets for comprehensive score analysis. We further integrate a Non-Chord-Tone prediction module, which identifies and excludes passing and non-functional notes from all tasks, thereby improving the consistency of label signals. Experimental evaluations demonstrate that AnalysisGNN achieves performance comparable to traditional static-dataset approaches, while showing increased resilience to domain shifts and annotation inconsistencies across multiple heterogeneous corpora.

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