Predicting the difficulty of playing a musical score is essential for structuring and exploring score collections. Despite its importance for music education, the automatic difficulty classification of piano scores is not yet solved, mainly due to the lack of annotated data and the subjectiveness of the annotations. This paper aims to advance the state-of-the-art in score difficulty classification with two major contributions. To address the lack of data, we present Can I Play It? (CIPI) dataset, a machine-readable piano score dataset with difficulty annotations obtained from the renowned classical music publisher Henle Verlag. The dataset is created by matching public domain scores with difficulty labels from Henle Verlag, then reviewed and corrected by an expert pianist. As a second contribution, we explore various input representations from score information to pre-trained ML models for piano fingering and expressiveness inspired by the musicology definition of performance. We show that combining the outputs of multiple classifiers performs better than the classifiers on their own, pointing to the fact that the representations capture different aspects of difficulty. In addition, we conduct numerous experiments that lay a foundation for score difficulty classification and create a basis for future research. Our best-performing model reports a 39.47% balanced accuracy and 1.13 median square error across the nine difficulty levels proposed in this study. Code, dataset, and models are made available for reproducibility.

翻译：预测乐谱的演奏难度对于组织和探索乐谱库至关重要。尽管这对音乐教育具有重要意义，但钢琴乐谱的自动难度分类仍未得到解决，主要原因是缺乏标注数据以及标注的主观性。本文旨在通过两项主要贡献推动乐谱难度分类的现有技术水平。为解决数据不足问题，我们提出了Can I Play It? (CIPI)数据集，这是一个包含来自著名古典音乐出版商Henle Verlag的难度标注的机器可读钢琴乐谱数据集。该数据集通过将公有领域乐谱与Henle Verlag的难度标签进行匹配，并由一位专家钢琴家审查和修正而创建。作为第二项贡献，我们借鉴音乐学对演奏的定义，探索了从乐谱信息到用于钢琴指法和表现力的预训练机器学习模型的各种输入表征。我们表明，组合多个分类器的输出比单独使用分类器表现更好，这表明这些表征捕捉了难度的不同方面。此外，我们进行了大量实验，为乐谱难度分类奠定了基础，并为未来研究创造了依据。我们表现最佳的模型在所提出的九个难度等级上实现了39.47%的平衡准确率和1.13的中位数平方误差。代码、数据集和模型已公开以支持可复现性。