We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensions of music, pitch and rhythm, and considers both relative and absolute representations of these components. We evaluate our approach on four different musical understanding problems: monophonic voice separation, harmonic analysis, cadence detection, and composer identification which, in abstract terms, translate to different graph learning problems, namely, node classification, link prediction, and graph classification. Our experiments demonstrate that MusGConv improves the performance on three of the aforementioned tasks while being conceptually very simple and efficient. We interpret this as evidence that it is beneficial to include perception-informed processing of fundamental musical concepts when developing graph network applications on musical score data.

翻译：我们提出了一种新型图卷积模块MusGConv，该方法专为高效处理乐谱数据而设计，其设计灵感源于通用感知原理。该模块聚焦音乐的两个基本维度——音高与节奏，并同时考虑这两种元素的相对与绝对表征。我们在四项不同的音乐理解任务上评估了该方法：单声部声部分离、和声分析、终止式检测与作曲家识别——抽象而言，这些任务分别对应不同的图学习问题，即节点分类、链接预测与图分类。实验结果表明，MusGConv在概念极为简洁且高效的同时，提升了上述三项任务的性能。我们将此解读为：在基于乐谱数据的图网络应用开发中，融入对基本音乐概念的感知驱动处理具有显著裨益。