# A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody

arXiv: https://arxiv.org/abs/2111.12986 · HTML: https://mlsanity.com/paper/2111.12986/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [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)
- [Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation](https://mlsanity.com/paper/1804.09399/) (2018)
- [A Hierarchical Recurrent Neural Network for Symbolic Melody Generation](https://mlsanity.com/paper/1712.05274/) (2017)
- [DeepJ: Style-Specific Music Generation](https://mlsanity.com/paper/1801.00887/) (2018)
- [MP3net: coherent, minute-long music generation from raw audio with a simple convolutional GAN](https://mlsanity.com/paper/2101.04785/) (2021)
- [BachProp: Learning to Compose Music in Multiple Styles](https://mlsanity.com/paper/1802.05162/) (2018)
- [Dual-track Music Generation using Deep Learning](https://mlsanity.com/paper/2005.04353/) (2020)

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

- [An LSTM-Based Chord Generation System Using Chroma Histogram Representations](https://mlsanity.com/paper/2405.05240/) (2024)
- [LoopGen: Training-Free Loopable Music Generation](https://mlsanity.com/paper/2504.04466/) (2025)

## Abstract (arXiv)

We present a method for the generation of Midi files of piano music. The method models the right and left hands using two networks, where the left hand is conditioned on the right hand. This way, the melody is generated before the harmony. The Midi is represented in a way that is invariant to the musical scale, and the melody is represented, for the purpose of conditioning the harmony, by the content of each bar, viewed as a chord. Finally, notes are added randomly, based on this chord representation, in order to enrich the generated audio. Our experiments show a significant improvement over the state of the art for training on such datasets, and demonstrate the contribution of each of the novel components.