# DeepJ: Style-Specific Music Generation

arXiv: https://arxiv.org/abs/1801.00887 · HTML: https://mlsanity.com/paper/1801.00887/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2018.

## Built on by (most cited first, 12 of 12)

- [MuseCoco: Generating Symbolic Music from Text](https://mlsanity.com/paper/2306.00110/) (2023)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [Audio representations for deep learning in sound synthesis: A review](https://mlsanity.com/paper/2201.02490/) (2022)
- [CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN](https://mlsanity.com/paper/2104.00353/) (2021)
- [Lyapunov-Guided Representation of Recurrent Neural Network Performance](https://mlsanity.com/paper/2204.04876/) (2022)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody](https://mlsanity.com/paper/2111.12986/) (2021)
- [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)
- [An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance](https://mlsanity.com/paper/2304.11521/) (2023)
- [MCMChaos: Improvising Rap Music with MCMC Methods and Chaos Theory](https://mlsanity.com/paper/2401.07967/) (2024)
- [A Novel Bi-LSTM And Transformer Architecture For Generating Tabla Music](https://mlsanity.com/paper/2404.05765/) (2024)

## Abstract (arXiv)

Recent advances in deep neural networks have enabled algorithms to compose music that is comparable to music composed by humans. However, few algorithms allow the user to generate music with tunable parameters. The ability to tune properties of generated music will yield more practical benefits for aiding artists, filmmakers, and composers in their creative tasks. In this paper, we introduce DeepJ - an end-to-end generative model that is capable of composing music conditioned on a specific mixture of composer styles. Our innovations include methods to learn musical style and music dynamics. We use our model to demonstrate a simple technique for controlling the style of generated music as a proof of concept. Evaluation of our model using human raters shows that we have improved over the Biaxial LSTM approach.