# CoCoFormer: A controllable feature-rich polyphonic music generation method

arXiv: https://arxiv.org/abs/2310.09843 · HTML: https://mlsanity.com/paper/2310.09843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 15 Oct 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [Bridging the Gap between Training and Inference for Neural Machine Translation](https://mlsanity.com/paper/1906.02448/) (2019)
- [DeepBach: a Steerable Model for Bach Chorales Generation](https://mlsanity.com/paper/1612.01010/) (2016)
- [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation](https://mlsanity.com/paper/1703.10847/) (2017)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://mlsanity.com/paper/1805.11749/) (2018)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [LakhNES: Improving multi-instrumental music generation with cross-domain pre-training](https://mlsanity.com/paper/1907.04868/) (2019)
- [Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures](https://mlsanity.com/paper/1808.08946/) (2018)
- [The NES Music Database: A multi-instrumental dataset with expressive performance attributes](https://mlsanity.com/paper/1806.04278/) (2018)
- [Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN](https://mlsanity.com/paper/1412.7927/) (2014)
- [Improving Polyphonic Music Models with Feature-Rich Encoding](https://mlsanity.com/paper/1911.11775/) (2019)
- [LSTM Based Music Generation System](https://mlsanity.com/paper/1908.01080/) (2019)
- [JS Fake Chorales: a Synthetic Dataset of Polyphonic Music with Human Annotation](https://mlsanity.com/paper/2107.10388/) (2021)
- [Attentional networks for music generation](https://mlsanity.com/paper/2002.03854/) (2020)

## Abstract (arXiv)

This paper explores the modeling method of polyphonic music sequence. Due to the great potential of Transformer models in music generation, controllable music generation is receiving more attention. In the task of polyphonic music, current controllable generation research focuses on controlling the generation of chords, but lacks precise adjustment for the controllable generation of choral music textures. This paper proposed Condition Choir Transformer (CoCoFormer) which controls the output of the model by controlling the chord and rhythm inputs at a fine-grained level. In this paper, the self-supervised method improves the loss function and performs joint training through conditional control input and unconditional input training. In order to alleviate the lack of diversity on generated samples caused by the teacher forcing training, this paper added an adversarial training method. CoCoFormer enhances model performance with explicit and implicit inputs to chords and rhythms. In this paper, the experiments proves that CoCoFormer has reached the current better level than current models. On the premise of specifying the polyphonic music texture, the same melody can also be generated in a variety of ways.