A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody
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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.
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
12 of 22 references.
References · 12
- 2017Attention Is All You Needcited by 60,361
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
- 2020Jukebox: A Generative Model for Musiccited by 542
- 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
- 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 91
- 2017MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generationcited by 72
- 2018Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generationcited by 20
- 2017A Hierarchical Recurrent Neural Network for Symbolic Melody Generationcited by 14
- 2018DeepJ: Style-Specific Music Generationcited by 12
- 2021MP3net: coherent, minute-long music generation from raw audio with a simple convolutional GANcited by 4
- 2018BachProp: Learning to Compose Music in Multiple Stylescited by 3
- 2020Dual-track Music Generation using Deep Learningcited by 1
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.