用图神经网络统一分析乐谱，提升跨数据集稳定性。
AnalysisGNN: Unified Music Analysis with Graph Neural Networks
- 构建图神经网络框架，融合多任务与数据洗牌策略
- 在多个异构数据集上达到与传统方法相当的性能
- 引入非和弦音模块，增强标签一致性，适合音乐分析研究者
近年来计算音乐分析发展迅速，但多数方法仅针对特定领域。本文提出AnalysisGNN，一种基于图神经网络的新框架，通过数据洗牌策略、自定义加权多任务损失及任务分类器的对数融合，整合异构标注的符号化乐谱数据，实现全面的乐谱分析。进一步集成非和弦音预测模块，识别并排除经过音等非功能性音符，从而提升标签信号的一致性。实验表明，AnalysisGNN性能与传统静态数据集方法相当，且在多个异构语料库上对领域迁移和标注不一致具有更强鲁棒性。
原文摘要 · Abstract (English)
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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