# Artificial Neural Networks Jamming on the Beat

arXiv: https://arxiv.org/abs/2007.06284 · HTML: https://mlsanity.com/paper/2007.06284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Language Models are Drummers: Drum Composition with Natural Language Pre-Training](https://mlsanity.com/paper/2301.01162/) (2023)
- [Machine Apophenia: The Kaleidoscopic Generation of Architectural Images](https://mlsanity.com/paper/2407.09172/) (2024)

## Abstract (arXiv)

This paper addresses the issue of long-scale correlations that is characteristic for symbolic music and is a challenge for modern generative algorithms. It suggests a very simple workaround for this challenge, namely, generation of a drum pattern that could be further used as a foundation for melody generation. The paper presents a large dataset of drum patterns alongside with corresponding melodies. It explores two possible methods for drum pattern generation. Exploring a latent space of drum patterns one could generate new drum patterns with a given music style. Finally, the paper demonstrates that a simple artificial neural network could be trained to generate melodies corresponding with these drum patters used as inputs. Resulting system could be used for end-to-end generation of symbolic music with song-like structure and higher long-scale correlations between the notes.