# Generative Deep Learning for Virtuosic Classical Music: Generative Adversarial Networks as Renowned Composers

arXiv: https://arxiv.org/abs/2101.00169 · HTML: https://mlsanity.com/paper/2101.00169/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)

## Abstract (arXiv)

Current AI-generated music lacks fundamental principles of good compositional techniques. By narrowing down implementation issues both programmatically and musically, we can create a better understanding of what parameters are necessary for a generated composition nearly indistinguishable from that of a master composer.