论文标题
Mospy：象征音乐的工具包
MusPy: A Toolkit for Symbolic Music Generation
论文作者
论文摘要
在本文中，我们介绍了Muspy，这是一个开源的Python图书馆，用于象征性音乐。 Muspy为音乐生成系统中的基本组件提供了易于使用的工具，包括数据集管理，数据I/O，数据预处理和模型评估。为了展示其潜力，我们对Muspy当前支持的11个数据集进行了统计分析。此外，我们通过在每个数据集上训练自回归模型并在其他数据集上测量持有的可能性，进行了跨数据集的概括实验，这是Muspy的数据集管理系统变得更容易的过程。结果提供了各种常用数据集之间的域重叠的地图，并表明某些数据集包含比其他数据集更具代表性的跨类型样本。除了数据集分析外，这些结果可能是在未来研究中选择数据集的指南。源代码和文档可从https://github.com/salu133445/muspy获得。
In this paper, we present MusPy, an open source Python library for symbolic music generation. MusPy provides easy-to-use tools for essential components in a music generation system, including dataset management, data I/O, data preprocessing and model evaluation. In order to showcase its potential, we present statistical analysis of the eleven datasets currently supported by MusPy. Moreover, we conduct a cross-dataset generalizability experiment by training an autoregressive model on each dataset and measuring held-out likelihood on the others---a process which is made easier by MusPy's dataset management system. The results provide a map of domain overlap between various commonly used datasets and show that some datasets contain more representative cross-genre samples than others. Along with the dataset analysis, these results might serve as a guide for choosing datasets in future research. Source code and documentation are available at https://github.com/salu133445/muspy .
