从音频编码器到钢琴评委：独奏钢琴演奏理解的基准测试
From Audio Encoders to Piano Judges: Benchmarking Performance Understanding for Solo Piano
AI总结：
本研究提出钢琴演奏标注数据集（PLD），利用预训练音频编码器（如Audio-MAE）微调，解决专业水平排名、难度估计和技巧检测任务，最佳准确率分别为93.6%、33.7%和46.7%，并揭示顶级演奏评估的挑战。
摘要：
我们的研究探讨了一种通过音频编码模型理解音乐演奏的方法，聚焦于西方古典钢琴独奏音乐领域。与调性或流派等作品层面的属性理解相比，我们识别出演奏层面音乐理解中的知识空白，并解决三个关键任务：专业水平排名、难度估计和钢琴技巧检测，为此引入了一个全面的钢琴演奏标注数据集（PLD）。我们利用预训练的音频编码器，特别是Jukebox、Audio-MAE、MERT和DAC，展示了它们在处理下游任务时的不同能力，以探索领域特定的微调是否能增强捕捉演奏细微差别的能力。我们的最佳方法在专业水平排名上达到93.6%的准确率，在难度估计上达到33.7%，在技巧检测上达到46.7%，其中Audio-MAE是整体上最有效的编码器。最后，我们使用训练好的模型对肖邦钢琴比赛数据进行了专业水平排名的案例研究，这突显了准确评估顶级演奏的挑战。
英文摘要：
Our study investigates an approach for understanding musical performances through the lens of audio encoding models, focusing on the domain of solo Western classical piano music. Compared to composition-level attribute understanding such as key or genre, we identify a knowledge gap in performance-level music understanding, and address three critical tasks: expertise ranking, difficulty estimation, and piano technique detection, introducing a comprehensive Pianism-Labelling Dataset (PLD) for this purpose. We leverage pre-trained audio encoders, specifically Jukebox, Audio-MAE, MERT, and DAC, demonstrating varied capabilities in tackling downstream tasks, to explore whether domain-specific fine-tuning enhances capability in capturing performance nuances. Our best approach achieved 93.6\% accuracy in expertise ranking, 33.7\% in difficulty estimation, and 46.7\% in technique detection, with Audio-MAE as the overall most effective encoder. Finally, we conducted a case study on Chopin Piano Competition data using trained models for expertise ranking, which highlights the challenge of accurately assessing top-tier performances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
