用乐谱信息修正自动音乐转录中的力度，提升表现力还原度。
Score-Informed Transformer for Refining MIDI Velocity in Automatic Music Transcription
- 基于乐谱的轻量Transformer模块，动态优化音符力度估计。
- 在MAESTRO上降低力度误差，跨数据集泛化能力显著提升。
- 仅增加100万参数，适配多类音乐转录系统，适合实际应用。
MIDI力度对捕捉人类演奏的表现力动态至关重要。在实际场景中，常可获得与演奏音频对应的、但力度不准确的乐谱（如音乐教育和免费在线资源），这催生了基于乐谱的力度估计任务。本文提出一种模块化、轻量级的得分引导式Transformer修正模块，用于优化自动音乐转录（AMT）系统的力度预测。将该模块集成至HPT、HPPNet和DynEst等多个AMT系统中。仅在MAESTRO训练集上训练，所提方法在MAESTRO上持续降低力度估计误差，并显著提升在SMD和MAPS数据集上的跨数据集泛化性能。在此训练协议下，与HPT结合的Score-HPT模型达到新最优效果，超越现有得分引导方法及含力度能力的AMT系统，同时仅增加100万参数。
原文摘要 · Abstract (English)
MIDI velocity is crucial for capturing expressive dynamics in human performances. In practical scenarios, a music score with inaccurate velocities may be available alongside the performance audio (e.g., music education and free online archives), enabling the task of score-informed MIDI velocity estimation. In this work, we propose a modular, lightweight score-informed Transformer correction module that refines the velocity estimates of Automatic Music Transcription (AMT) systems. We integrate the proposed module into multiple AMT systems (HPT, HPPNet, and DynEst). Trained exclusively on the MAESTRO training split, our method consistently reduces velocity estimation errors on MAESTRO and improves cross-dataset generalization to SMD and MAPS datasets. Under this training protocol, integrating our score-informed module with HPT (named Score-HPT) establishes a new state-of-the-art performance, outperforms existing score-informed methods and velocity-enabled AMT systems while adding only 1 M parameters.
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