  We propose a framework for audio-to-score alignment on piano performance that
employs automatic music transcription (AMT) using neural networks. Even though
the AMT result may contain some errors, the note prediction output can be
regarded as a learned feature representation that is directly comparable to
MIDI note or chroma representation. To this end, we employ two recurrent neural
networks that work as the AMT-based feature extractors to the alignment
algorithm. One predicts the presence of 88 notes or 12 chroma in frame-level
and the other detects note onsets in 12 chroma. We combine the two types of
learned features for the audio-to-score alignment. For comparability, we apply
dynamic time warping as an alignment algorithm without any additional
post-processing. We evaluate the proposed framework on the MAPS dataset and
compare it to previous work. The result shows that the alignment framework with
the learned features significantly improves the accuracy, achieving less than
10 ms in mean onset error.
