# Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network

arXiv: https://arxiv.org/abs/1807.11161 · HTML: https://mlsanity.com/paper/1807.11161/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework](https://mlsanity.com/paper/2104.13056/) (2021)
- [JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE](https://mlsanity.com/paper/2210.06007/) (2022)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [Conditional Drums Generation using Compound Word Representations](https://mlsanity.com/paper/2202.04464/) (2022)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [Etude: Piano Cover Generation with a Three-Stage Approach -- Extract, strucTUralize, and DEcode](https://mlsanity.com/paper/2509.16522/) (2025)

## Abstract (arXiv)

Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Audio samples of the generated result can be found at https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk