  In this paper we propose a novel generative approach, DiffRoll, to tackle
automatic music transcription (AMT). Instead of treating AMT as a
discriminative task in which the model is trained to convert spectrograms into
piano rolls, we think of it as a conditional generative task where we train our
model to generate realistic looking piano rolls from pure Gaussian noise
conditioned on spectrograms. This new AMT formulation enables DiffRoll to
transcribe, generate and even inpaint music. Due to the classifier-free nature,
DiffRoll is also able to be trained on unpaired datasets where only piano rolls
are available. Our experiments show that DiffRoll outperforms its
discriminative counterpart by 19 percentage points (ppt.) and our ablation
studies also indicate that it outperforms similar existing methods by 4.8 ppt.
  Source code and demonstration are available https://sony.github.io/DiffRoll/.
