论文标题
学习层次的度量结构超出措施
Learning Hierarchical Metrical Structure Beyond Measures
论文作者
论文摘要
音乐包含超出节拍和措施的层次结构。虽然层次结构注释有助于音乐信息检索和计算机音乐学，但在当前的数字音乐数据库中，这种注释很少。在本文中，我们探讨了一种数据驱动的方法，以自动从分数中提取层次度量结构。我们提出了一个具有时间卷积网络条件随机场（TCN-CRF）体系结构的新模型。给定符号音乐得分，我们的模型以式量化的形式采用任意数量的声音，并预测了从偏低级别到截面级别的4级层次级别结构。我们还使用RWC-POP MIDI文件来注释数据集，以促进培训和评估。我们通过实验表明，在不同的编排设置下，所提出的方法的性能优于基于规则的方法。我们还对模型预测进行了一些简单的音乐分析。所有演示，数据集和预培训模型均在GitHub上公开可用。
Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate training and evaluation. We show by experiments that the proposed method performs better than the rule-based approach under different orchestration settings. We also perform some simple musicological analysis on the model predictions. All demos, datasets and pre-trained models are publicly available on Github.
