This article motivates, describes, and presents the PBSCSR dataset for studying composer style recognition of piano sheet music. Our overarching goal was to create a dataset for studying composer style recognition that is "as accessible as MNIST and as challenging as ImageNet." To achieve this goal, we sample fixed-length bootleg score fragments from piano sheet music images on IMSLP. The dataset itself contains 40,000 62x64 bootleg score images for a 9-way classification task, 100,000 62x64 bootleg score images for a 100-way classification task, and 29,310 unlabeled variable-length bootleg score images for pretraining. The labeled data is presented in a form that mirrors MNIST images, in order to make it extremely easy to visualize, manipulate, and train models in an efficient manner. Additionally, we include relevant metadata to allow access to the underlying raw sheet music images and other related data on IMSLP. We describe several research tasks that could be studied with the dataset, including variations of composer style recognition in a few-shot or zero-shot setting. For tasks that have previously proposed models, we release code and baseline results for future works to compare against. We also discuss open research questions that the PBSCSR data is especially well suited to facilitate research on and areas of fruitful exploration in future work.

翻译：本文介绍、描述并提出了PBSCSR数据集，用于研究钢琴乐谱的作曲家风格识别。我们的总体目标是创建一个既"像MNIST一样易于使用，又像ImageNet一样具有挑战性"的作曲家风格识别研究数据集。为实现这一目标，我们从IMSLP上的钢琴乐谱图像中采样了固定长度的翻录乐谱片段。该数据集包含：用于9类分类任务的40,000张62×64翻录乐谱图像，用于100类分类任务的100,000张62×64翻录乐谱图像，以及用于预训练的29,310张未标记的变长翻录乐谱图像。标记数据以模仿MNIST图像的形式呈现，以便于高效地可视化、操作和训练模型。此外，我们还提供了相关元数据，以便访问IMSLP上的原始乐谱图像及其他相关数据。我们描述了该数据集可支持的若干研究任务，包括少样本或零样本场景下的作曲家风格识别变体。对于已有先前提出模型的任务，我们发布了代码和基线结果，供未来研究进行对比。我们还讨论了PBSCSR数据特别适合促进研究以及未来工作中值得探索的开放性问题。