# Everybody Compose: Deep Beats To Music

arXiv: https://arxiv.org/abs/2306.06284 · HTML: https://mlsanity.com/paper/2306.06284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https://mlsanity.com/paper/1801.02753/) (2018)
- [Text-based LSTM networks for Automatic Music Composition](https://mlsanity.com/paper/1604.05358/) (2016)

## Abstract (arXiv)

This project presents a deep learning approach to generate monophonic melodies based on input beats, allowing even amateurs to create their own music compositions. Three effective methods - LSTM with Full Attention, LSTM with Local Attention, and Transformer with Relative Position Representation - are proposed for this novel task, providing great variation, harmony, and structure in the generated music. This project allows anyone to compose their own music by tapping their keyboards or ``recoloring'' beat sequences from existing works.