论文标题
ASMD：用于编译具有音频和分数的多模式数据集的自动框架
ASMD: an automatic framework for compiling multimodal datasets with audio and scores
论文作者
论文摘要
本文介绍了用于处理音乐处理任务数据集的开源Python框架，其目的是提高音乐计算中研究项目的可重复性并评估机器学习模型的概括能力。该框架可以自动下载并安装几个常用的数据集用于多模式音乐处理。具体而言，我们提供了一个Python API，可以根据特定属性（例如，作曲家，工具等的交叉点和工会等）通过布尔集设置操作访问数据集。该框架旨在简化包含新数据集和各自的基础真实注释，以便可以构建，转换和扩展自己的收集，并通过合规格式分配它，以利用API。所有代码和地面真相均在适当的开放许可下发布。
This paper describes an open-source Python framework for handling datasets for music processing tasks, built with the aim of improving the reproducibility of research projects in music computing and assessing the generalization abilities of machine learning models. The framework enables the automatic download and installation of several commonly used datasets for multimodal music processing. Specifically, we provide a Python API to access the datasets through Boolean set operations based on particular attributes, such as intersections and unions of composers, instruments, and so on. The framework is designed to ease the inclusion of new datasets and the respective ground-truth annotations so that one can build, convert, and extend one's own collection as well as distribute it by means of a compliant format to take advantage of the API. All code and ground-truth are released under suitable open licenses.
