# Performing Structured Improvisations with pre-trained Deep Learning Models

arXiv: https://arxiv.org/abs/1904.13285 · HTML: https://mlsanity.com/paper/1904.13285/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Musical Speech: A Transformer-based Composition Tool](https://mlsanity.com/paper/2108.01043/) (2021)
- [Losses, Dissonances, and Distortions](https://mlsanity.com/paper/2111.05128/) (2021)
- [Notochord: a Flexible Probabilistic Model for Real-Time MIDI Performance](https://mlsanity.com/paper/2403.12000/) (2024)

## Abstract (arXiv)

The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in "offline" mode, with few restrictions on the processing time. Integrating these types of models into a live structured performance poses a challenge because of the necessity to respect the beat and harmony. Further, these deep models tend to be agnostic to the style of a performer, which often renders them impractical for live performance. In this paper we propose a system which enables the integration of out-of-the-box generative models by leveraging the musician's creativity and expertise.