In this work, we present Score MUsic Graph (SMUG)-Explain, a framework for generating and visualizing explanations of graph neural networks applied to arbitrary prediction tasks on musical scores. Our system allows the user to visualize the contribution of input notes (and note features) to the network output, directly in the context of the musical score. We provide an interactive interface based on the music notation engraving library Verovio. We showcase the usage of SMUG-Explain on the task of cadence detection in classical music. All code is available on https://github.com/manoskary/SMUG-Explain.

翻译：本文提出了一种名为“乐谱图”（SMUG）-Explain的框架，用于生成和可视化图神经网络对乐谱上任意预测任务的解释结果。该系统允许用户直接在乐谱上下文中，可视化输入音符（及其特征）对网络输出的贡献。我们基于音乐符号雕刻库Verovio构建了交互式界面，并以古典音乐中的终止式检测任务为例展示了SMUG-Explain的应用。所有代码均可在https://github.com/manoskary/SMUG-Explain获取。