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

DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability