# Toward Deep Drum Source Separation

arXiv: https://arxiv.org/abs/2312.09663 · HTML: https://mlsanity.com/paper/2312.09663/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [The 2018 Signal Separation Evaluation Campaign](https://mlsanity.com/paper/1804.06267/) (2018)
- [Universal Sound Separation](https://mlsanity.com/paper/1905.03330/) (2019)
- [Music Demixing Challenge 2021](https://mlsanity.com/paper/2108.13559/) (2021)
- [Learning to Groove with Inverse Sequence Transformations](https://mlsanity.com/paper/1905.06118/) (2019)
- [All for One and One for All: Improving Music Separation by Bridging Networks](https://mlsanity.com/paper/2010.04228/) (2020)
- [The Sound Demixing Challenge 2023 unicodex2013 Music Demixing Track](https://mlsanity.com/paper/2308.06979/) (2023)
- [Meta-learning Extractors for Music Source Separation](https://mlsanity.com/paper/2002.07016/) (2020)
- [Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset](https://mlsanity.com/paper/2004.00188/) (2020)
- [Deep Unsupervised Drum Transcription](https://mlsanity.com/paper/1906.03697/) (2019)
- [Towards multi-instrument drum transcription](https://mlsanity.com/paper/1806.06676/) (2018)
- [Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms](https://mlsanity.com/paper/2105.05791/) (2021)

## Built on by (most cited first, 4 of 4)

- [The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis](https://mlsanity.com/paper/2505.03337/) (2025)
- [Towards Realistic Synthetic Data for Automatic Drum Transcription](https://mlsanity.com/paper/2601.09520/) (2026)
- [Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis](https://mlsanity.com/paper/2605.14555/) (2026)
- [Separate-and-Detect: Unified Drum Transcription and Stem Generation via Latent Diffusion](https://mlsanity.com/paper/2608.01093/) (2026)

## Abstract (arXiv)

In the past, the field of drum source separation faced significant challenges due to limited data availability, hindering the adoption of cutting-edge deep learning methods that have found success in other related audio applications. In this manuscript, we introduce StemGMD, a large-scale audio dataset of isolated single-instrument drum stems. Each audio clip is synthesized from MIDI recordings of expressive drums performances using ten real-sounding acoustic drum kits. Totaling 1224 hours, StemGMD is the largest audio dataset of drums to date and the first to comprise isolated audio clips for every instrument in a canonical nine-piece drum kit. We leverage StemGMD to develop LarsNet, a novel deep drum source separation model. Through a bank of dedicated U-Nets, LarsNet can separate five stems from a stereo drum mixture faster than real-time and is shown to significantly outperform state-of-the-art nonnegative spectro-temporal factorization methods.