AnalysisGNN: Unified Music Analysis with Graph Neural Networks

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, 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. The full source code for this work is available at: [github.com/manoskary/AnalysisGNN]