Capturing intricate and subtle variations in human expressiveness in music performance using computational approaches is challenging. In this paper, we propose a novel approach for reconstructing human expressiveness in piano performance with a multi-layer bi-directional Transformer encoder. To address the needs for large amounts of accurately captured and score-aligned performance data in training neural networks, we use transcribed scores obtained from an existing transcription model to train our model. We integrate pianist identities to control the sampling process and explore the ability of our system to model variations in expressiveness for different pianists. The system is evaluated through statistical analysis of generated expressive performances and a listening test. Overall, the results suggest that our method achieves state-of-the-art in generating human-like piano performances from transcribed scores, while fully and consistently reconstructing human expressiveness poses further challenges.

翻译：捕捉音乐表演中人类表现力的复杂微妙变化是一项计算挑战。本文提出了一种利用多层双向Transformer编码器重建钢琴演奏中人类表现力的新方法。针对神经网络训练对大量精确捕捉且与乐谱对齐的表演数据的需求，我们采用现有转录模型生成的转录乐谱来训练模型。通过整合钢琴家身份标识以控制采样过程，探索系统对不同钢琴家表现力变化建模的能力。通过生成性表演的统计分析及听力测试对系统进行评估。总体结果表明，我们的方法在基于转录乐谱生成类人钢琴表演方面达到了当前最优水平，但完全且一致地重建人类表现力仍面临进一步挑战。