Symbolic Music Alignment is the process of matching performed MIDI notes to
corresponding score notes. In this paper, we introduce a reinforcement learning
(RL)-based online symbolic music alignment technique. The RL agent - an
attention-based neural network - iteratively estimates the current score
position from local score and performance contexts. For this symbolic alignment
task, environment states can be sampled exhaustively and the reward is dense,
rendering a formulation as a simplified offline RL problem straightforward. We
evaluate the trained agent in three ways. First, in its capacity to identify
correct score positions for sampled test contexts; second, as the core
technique of a complete algorithm for symbolic online note-wise alignment; and
finally, as a real-time symbolic score follower. We further investigate the
pitch-based score and performance representations used as the agent's inputs.
To this end, we develop a second model, a two-step Dynamic Time Warping
(DTW)-based offline alignment algorithm leveraging the same input
representation. The proposed model outperforms a state-of-the-art reference
model of offline symbolic music alignment.

该研究介绍了一种基于强化学习的在线符号音乐对齐技术，利用注意力机制的神经网络估计乐谱位置，并通过三种方式进行评估，优于当前最先进的离线符号音乐对齐模型。