  Note alignment refers to the task of matching individual notes of two
versions of the same symbolically encoded piece. Methods addressing this task
commonly rely on sequence alignment algorithms such as Hidden Markov Models or
Dynamic Time Warping (DTW) applied directly to note or onset sequences. While
successful in many cases, such methods struggle with large mismatches between
the versions. In this work, we learn note-wise representations from data
augmented with various complex mismatch cases, e.g. repeats, skips, block
insertions, and long trills. At the heart of our approach lies a transformer
encoder network - TheGlueNote - which predicts pairwise note similarities for
two 512 note subsequences. We postprocess the predicted similarities using
flavors of weightedDTW and pitch-separated onsetDTW to retrieve note matches
for two sequences of arbitrary length. Our approach performs on par with the
state of the art in terms of note alignment accuracy, is considerably more
robust to version mismatches, and works directly on any pair of MIDI files.
