  Audio-to-score alignment (A2SA) is a multimodal task consisting in the
alignment of audio signals to music scores. Recent literature confirms the
benefits of Automatic Music Transcription (AMT) for A2SA at the frame-level. In
this work, we aim to elaborate on the exploitation of AMT Deep Learning (DL)
models for achieving alignment at the note-level. We propose a method which
benefits from HMM-based score-to-score alignment and AMT, showing a remarkable
advancement beyond the state-of-the-art. We design a systematic procedure to
take advantage of large datasets which do not offer an aligned score. Finally,
we perform a thorough comparison and extensive tests on multiple datasets.
