Optimizing DTW-based audio-to-MIDI alignment and matching
Dynamic time warping (DTW) has proven to be an extremely effective method for both aligning and matching recordings of music to corresponding MIDI transcriptions. However, its performance is heavily affected by factors such as the representation used for the audio and MIDI data and its adjustable parameters. We therefore investigate automatically optimizing the design of DTW-based alignment and matching systems. Our approach uses Bayesian optimization to tune system design and parameters over a synthetically-created dataset of audio and MIDI pairs. We then perform an exhaustive search over DTW score normalization techniques to find the optimal method for reporting a reliable alignment confidence score, as required in matching tasks. This results in a DTW-based system which is conceptually simple and highly accurate at both alignment and matching. We verified that this system achieves high performance in a large-scale qualitative evaluation of real-world alignments.