标题： 使用变换网络重建钢琴表演中的人类表现力 显示英文标题

标题： Reconstructing Human Expressiveness in Piano Performances with a Transformer Network

摘要： 使用计算方法捕捉音乐表演中人类表现力的复杂且微妙的变化是一项挑战。 本文提出了一种新颖的方法，利用多层双向 Transformer 编码器重建钢琴演奏中的情感表达。 为了解决训练神经网络时需要大量准确捕获和音符对齐的表演数据的问题，我们使用现有的转录模型获得的转录乐谱来训练我们的模型。 我们将钢琴家的身份纳入采样过程，并探索我们的系统建模不同钢琴家表现力变化的能力。 通过生成的富有表现力的表演的统计分析和听觉测试对系统进行评估。 总体而言，结果表明，我们的方法在从转录乐谱生成类人钢琴演奏方面达到了最先进的水平，但完全且一致地重建人类表现力仍面临进一步的挑战。 显示英文摘要

摘要： 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.