# Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation

arXiv: https://arxiv.org/abs/1804.09399 · HTML: https://mlsanity.com/paper/1804.09399/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 20 of 20)

- [MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE](https://mlsanity.com/paper/2105.04090/) (2021)
- [Multitrack Music Transformer](https://mlsanity.com/paper/2207.06983/) (2022)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Music Composition with Deep Learning: A Review](https://mlsanity.com/paper/2108.12290/) (2021)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [Generating music with sentiment using Transformer-GANs](https://mlsanity.com/paper/2212.11134/) (2022)
- [GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers](https://mlsanity.com/paper/2302.05393/) (2023)
- [YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations](https://mlsanity.com/paper/2211.07131/) (2022)
- [Generating symbolic music using diffusion models](https://mlsanity.com/paper/2303.08385/) (2023)
- [ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production](https://mlsanity.com/paper/2307.05328/) (2023)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models](https://mlsanity.com/paper/2307.13005/) (2023)
- [Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music](https://mlsanity.com/paper/2407.21615/) (2024)
- [A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody](https://mlsanity.com/paper/2111.12986/) (2021)
- [SinTra: Learning an inspiration model from a single multi-track music segment](https://mlsanity.com/paper/2204.09917/) (2022)
- [Instrument Separation of Symbolic Music by Explicitly Guided Diffusion Model](https://mlsanity.com/paper/2209.02696/) (2022)
- [From Words to Music: A Study of Subword Tokenization Techniques in Symbolic Music Generation](https://mlsanity.com/paper/2304.08953/) (2023)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT](https://mlsanity.com/paper/2409.00919/) (2024)
- [MusicAIR: A Multimodal AI Music Generation Framework Powered by an Algorithm-Driven Core](https://mlsanity.com/paper/2511.17323/) (2025)

## Abstract (arXiv)

It has been shown recently that deep convolutional generative adversarial networks (GANs) can learn to generate music in the form of piano-rolls, which represent music by binary-valued time-pitch matrices. However, existing models can only generate real-valued piano-rolls and require further post-processing, such as hard thresholding (HT) or Bernoulli sampling (BS), to obtain the final binary-valued results. In this paper, we study whether we can have a convolutional GAN model that directly creates binary-valued piano-rolls by using binary neurons. Specifically, we propose to append to the generator an additional refiner network, which uses binary neurons at the output layer. The whole network is trained in two stages. Firstly, the generator and the discriminator are pretrained. Then, the refiner network is trained along with the discriminator to learn to binarize the real-valued piano-rolls the pretrained generator creates. Experimental results show that using binary neurons instead of HT or BS indeed leads to better results in a number of objective measures. Moreover, deterministic binary neurons perform better than stochastic ones in both objective measures and a subjective test. The source code, training data and audio examples of the generated results can be found at https://salu133445.github.io/bmusegan/ .