Online Symbolic Music Alignment with Offline Reinforcement Learning
Silvan David Peter
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.