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.

我们提出了一种新的图卷积块，称为 MusGConv，专门为高效处理音乐乐谱数据而设计，并根据一般感知原理进行了激发。它关注音乐的两个基本维度，音高和节奏，并考虑了这些组成部分的相对和绝对表示。我们在四个不同的音乐理解问题上评估了我们的方法：单声部分离、和声分析、乐句检测和作曲家识别，这在抽象术语中转化为不同的图学习问题，即节点分类、链接预测和图分类。我们的实验证明了 MusGConv 在以上三个任务上改善了性能，同时概念上非常简单和高效。我们解释这一结果是证据表明，在开发音乐乐谱数据的图网络应用时，包括基本音乐概念的感知驱动处理是有益的。