DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability
Abstract
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/.
Cite
@article{arxiv.2210.05148,
title = {DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability},
author = {Kin Wai Cheuk and Ryosuke Sawata and Toshimitsu Uesaka and Naoki Murata and Naoya Takahashi and Shusuke Takahashi and Dorien Herremans and Yuki Mitsufuji},
journal= {arXiv preprint arXiv:2210.05148},
year = {2024}
}