Park Jisoo, Kim Jongho, Park Jeong Mi, Choi Ahyeon, Li Wen-Syan, Park Jonghwa, Hwang Seung-Won
Graduate School of Data Science, Seoul National University, Seoul, 08826, Korea.
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, 08826, Korea.
Sci Rep. 2024 Oct 3;14(1):23002. doi: 10.1038/s41598-024-73810-0.
This study aims to build a comprehensive dataset that enables the automatic evaluation of piano performances. In real-world piano performance, especially within the realm of classical piano music, we encounter a vast spectrum of performance variations. The challenge lies in how to effectively evaluate these performances. We must consider three critical aspects: (1) It is essential to gauge how performers express the music and how listeners perceive the performance, rather than focusing on the compositional characteristics of the musical piece. (2) Beyond fundamental elements like pitch and duration, we must also embrace higher-level features such as interpretation. (3) Such evaluation should be done by experts to discern the nuanced performances. Regrettably, there exists no dataset that addresses these challenging evaluation tasks. Therefore, we introduce a pioneering dataset PercePiano, annotated by music experts, with more extensive features capable of representing these nuanced aspects effectively. It encapsulates piano performance with a wide range of perceptual features that are recognized by musicians. Our evaluation benchmark includes a novel metric designed to accommodate the inherent subjectivity of perception. Furthermore, we propose an enhanced baseline framework that grounds performance on score data, aligning model predictions with human perception. Harnessing the aligned features enhances the baseline performance and proves to be adaptable to various model structures. In conclusion, our research opens new possibilities for comprehensive piano performance evaluation.
本研究旨在构建一个能够对钢琴演奏进行自动评估的综合数据集。在现实世界的钢琴演奏中，尤其是在古典钢琴音乐领域，我们会遇到各种各样的演奏变化。挑战在于如何有效地评估这些演奏。我们必须考虑三个关键方面：（1）至关重要的是衡量演奏者如何表达音乐以及听众如何感知演奏，而不是关注音乐作品的作曲特征。（2）除了音高和时长等基本要素外，我们还必须纳入诸如诠释等更高层次的特征。（3）这种评估应由专家来进行，以辨别细微的演奏差异。遗憾的是，目前还没有一个数据集能够解决这些具有挑战性的评估任务。因此，我们引入了一个开创性的数据集PercePiano，由音乐专家进行注释，具有更广泛的特征，能够有效地代表这些细微的方面。它用音乐家认可的广泛感知特征封装了钢琴演奏。我们的评估基准包括一个旨在适应感知固有主观性的新颖指标。此外，我们提出了一个增强的基线框架，该框架基于乐谱数据进行性能评估，使模型预测与人类感知保持一致。利用对齐的特征可提高基线性能，并证明适用于各种模型结构。总之，我们的研究为全面的钢琴演奏评估开辟了新的可能性。
