Piano audio-to-score transcription (A2S) is an important yet underexplored
task with extensive applications for music composition, practice, and analysis.
However, existing end-to-end piano A2S systems faced difficulties in retrieving
bar-level information such as key and time signatures, and have been trained
and evaluated with only synthetic data. To address these limitations, we
propose a sequence-to-sequence (Seq2Seq) model with a hierarchical decoder that
aligns with the hierarchical structure of musical scores, enabling the
transcription of score information at both the bar and note levels by
multi-task learning. To bridge the gap between synthetic data and recordings of
human performance, we propose a two-stage training scheme, which involves
pre-training the model using an expressive performance rendering (EPR) system
on synthetic audio, followed by fine-tuning the model using recordings of human
performance. To preserve the voicing structure for score reconstruction, we
propose a pre-processing method for **Kern scores in scenarios with an
unconstrained number of voices. Experimental results support the effectiveness
of our proposed approaches, in terms of both transcription performance on
synthetic audio data in comparison to the current state-of-the-art, and the
first experiment on human recordings.

基于序列到序列模型的钢琴音频转谱任务中使用层次解码器，结合多任务学习实现对小节和音符级别音符信息的转录，同时通过预处理方法保留谱表结构以重构声部；通过两阶段训练方案，首先在合成音频上进行预训练，然后在人类演奏录音上进行微调从而弥补合成数据与人类演奏录音之间的差距。实验结果表明，该方法在合成音频数据与当前最先进技术相比的转录性能以及对人类演奏录音的首次实验方面具有良好效果。