# Deep Unsupervised Drum Transcription

arXiv: https://arxiv.org/abs/1906.03697 · HTML: https://mlsanity.com/paper/1906.03697/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data](https://mlsanity.com/paper/2107.04954/) (2021)
- [DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability](https://mlsanity.com/paper/2210.05148/) (2022)
- [Toward Deep Drum Source Separation](https://mlsanity.com/paper/2312.09663/) (2023)
- [Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models](https://mlsanity.com/paper/2404.06818/) (2024)
- [The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis](https://mlsanity.com/paper/2505.03337/) (2025)
- [MuScriptor: An Open Model for Multi-Instrument Music Transcription](https://mlsanity.com/paper/2607.08168/) (2026)
- [Music Transcription with (Almost) No Supervision](https://mlsanity.com/paper/2605.24193/) (2026)

## Abstract (arXiv)

We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner. DrummerNet does not require any ground-truth transcription and, with the data-scalability of deep neural networks, learns from a large unlabeled dataset. In DrummerNet, the target drum signal is first passed to a (trainable) transcriber, then reconstructed in a (fixed) synthesizer according to the transcription estimate. By training the system to minimize the distance between the input and the output audio signals, the transcriber learns to transcribe without ground truth transcription. Our experiment shows that DrummerNet performs favorably compared to many other recent drum transcription systems, both supervised and unsupervised.