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.

翻译：符号音乐对齐是将演奏的MIDI音符与对应的乐谱音符进行匹配的过程。本文提出了一种基于强化学习（RL）的在线符号音乐对齐技术。该RL智能体——一种基于注意力机制的神经网络——通过局部乐谱与演奏上下文迭代估计当前乐谱位置。针对此符号对齐任务，环境状态可被穷举采样且奖励密集，因此可直接将其简化为离线RL问题。我们从三个维度评估训练后的智能体：首先，检验其对采样测试上下文正确识别乐谱位置的能力；其次，将其作为完整在线逐音符符号对齐算法的核心技术；最后，评估其作为实时符号乐谱跟随器的性能。我们进一步探究了作为智能体输入的基于音高的乐谱与演奏表征。为此，我们开发了第二个模型——一种基于两步动态时间规整（DTW）的离线对齐算法，该算法采用相同的输入表征。实验表明，所提模型超越了当前最先进的离线符号音乐对齐参考模型。