# Deep Learning for Music

arXiv: https://arxiv.org/abs/1606.04930 · HTML: https://mlsanity.com/paper/1606.04930/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges](https://mlsanity.com/paper/2308.00031/) (2023)
- [Deep Ensemble Art Style Recognition](https://mlsanity.com/paper/2405.11675/) (2024)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation](https://mlsanity.com/paper/2506.08570/) (2025)
- [Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models](https://mlsanity.com/paper/2211.00731/) (2022)

## Abstract (arXiv)

Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNN-RBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone.