# From Artificial Neural Networks to Deep Learning for Music Generation -- History, Concepts and Trends

arXiv: https://arxiv.org/abs/2004.03586 · HTML: https://mlsanity.com/paper/2004.03586/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://mlsanity.com/paper/2302.03917/) (2023)
- [Byte Pair Encoding for Symbolic Music](https://mlsanity.com/paper/2301.11975/) (2023)
- [Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation](https://mlsanity.com/paper/2404.17157/) (2024)
- [Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task](https://mlsanity.com/paper/2211.11216/) (2022)
- [The GigaMIDI Dataset with Features for Expressive Music Performance Detection](https://mlsanity.com/paper/2502.17726/) (2025)
- [Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings](https://mlsanity.com/paper/2409.07827/) (2024)
- [The Power of Fragmentation: A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation](https://mlsanity.com/paper/2205.08579/) (2022)
- [Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANs](https://mlsanity.com/paper/2206.13787/) (2022)
- [An adaptive music generation architecture for games based on the deep learning Transformer mode](https://mlsanity.com/paper/2207.01698/) (2022)
- [JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting](https://mlsanity.com/paper/2307.09670/) (2023)
- [Downstream Fairness Caveats with Synthetic Healthcare Data](https://mlsanity.com/paper/2203.04462/) (2022)
- [Perception of AI-Generated Music -- The Role of Composer Identity, Personality Traits, Music Preferences, and Perceived Humanness](https://mlsanity.com/paper/2512.02785/) (2025)
- [A novel LSTM music generator based on the fractional time-frequency feature extraction](https://mlsanity.com/paper/2604.17823/) (2026)
- [Challenges for Musical Education in the Age of AI and Digital Transformation](https://mlsanity.com/paper/2608.05176/) (2026)

## Abstract (arXiv)

The current wave of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation. A growing area of application is the generation of creative content, notably the case of music, the topic of this paper. The motivation is in using the capacity of modern deep learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This paper provides a tutorial on music generation based on deep learning techniques. After a short introduction to the topic illustrated by a recent exemple, the paper analyzes some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions have prefigured current techniques. Then, we introduce some conceptual framework to analyze the various concepts and dimensions involved. Various examples of recent systems are introduced and analyzed to illustrate the variety of concerns and of techniques.