# Interactive Music Generation with Positional Constraints using Anticipation-RNNs

arXiv: https://arxiv.org/abs/1709.06404 · HTML: https://mlsanity.com/paper/1709.06404/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Music Composition with Deep Learning: A Review](https://mlsanity.com/paper/2108.12290/) (2021)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN](https://mlsanity.com/paper/2104.00353/) (2021)
- [An adaptive music generation architecture for games based on the deep learning Transformer mode](https://mlsanity.com/paper/2207.01698/) (2022)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [Music Generation using Three-layered LSTM](https://mlsanity.com/paper/2105.09046/) (2021)

## Abstract (arXiv)

Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This paper introduces a novel architecture called Anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined positional constraints. We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the Anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.