# High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction

arXiv: https://arxiv.org/abs/1908.00948 · HTML: https://mlsanity.com/paper/1908.00948/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [SingSong: Generating musical accompaniments from singing](https://mlsanity.com/paper/2301.12662/) (2023)
- [Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models](https://mlsanity.com/paper/2406.08384/) (2024)
- [V2Meow: Meowing to the Visual Beat via Video-to-Music Generation](https://mlsanity.com/paper/2305.06594/) (2023)
- [JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE](https://mlsanity.com/paper/2210.06007/) (2022)
- [Improving Musical Accompaniment Co-creation via Diffusion Transformers](https://mlsanity.com/paper/2410.23005/) (2024)
- [SampleMatch: Drum Sample Retrieval by Musical Context](https://mlsanity.com/paper/2208.01141/) (2022)
- [Conditional Drums Generation using Compound Word Representations](https://mlsanity.com/paper/2202.04464/) (2022)
- [Multi-Track MusicLDM: Towards Versatile Music Generation with Latent Diffusion Model](https://mlsanity.com/paper/2409.02845/) (2024)
- [CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN](https://mlsanity.com/paper/2104.00353/) (2021)
- [Language Models are Drummers: Drum Composition with Natural Language Pre-Training](https://mlsanity.com/paper/2301.01162/) (2023)
- [LiLAC: A Lightweight Latent ControlNet for Musical Audio Generation](https://mlsanity.com/paper/2506.11476/) (2025)
- [Towards Real-Time Human-AI Musical Co-Performance: Accompaniment Generation with Latent Diffusion Models and MAX/MSP](https://mlsanity.com/paper/2604.07612/) (2026)
- [Motif-Centric Representation Learning for Symbolic Music](https://mlsanity.com/paper/2309.10597/) (2023)
- [JAMMIN-GPT: Text-based Improvisation using LLMs in Ableton Live](https://mlsanity.com/paper/2312.03479/) (2023)

## Abstract (arXiv)

Spurred by the potential of deep learning, computational music generation has gained renewed academic interest. A crucial issue in music generation is that of user control, especially in scenarios where the music generation process is conditioned on existing musical material. Here we propose a model for conditional kick drum track generation that takes existing musical material as input, in addition to a low-dimensional code that encodes the desired relation between the existing material and the new material to be generated. These relational codes are learned in an unsupervised manner from a music dataset. We show that codes can be sampled to create a variety of musically plausible kick drum tracks and that the model can be used to transfer kick drum patterns from one song to another. Lastly, we demonstrate that the learned codes are largely invariant to tempo and time-shift.