# Music Generation using Three-layered LSTM

arXiv: https://arxiv.org/abs/2105.09046 · HTML: https://mlsanity.com/paper/2105.09046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 4 of 4)

- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Interactive Music Generation with Positional Constraints using Anticipation-RNNs](https://mlsanity.com/paper/1709.06404/) (2017)
- [Generating Music using an LSTM Network](https://mlsanity.com/paper/1804.07300/) (2018)

## Abstract (arXiv)

This paper explores the idea of utilising Long Short-Term Memory neural networks (LSTMNN) for the generation of musical sequences in ABC notation. The proposed approach takes ABC notations from the Nottingham dataset and encodes it to be fed as input for the neural networks. The primary objective is to input the neural networks with an arbitrary note, let the network process and augment a sequence based on the note until a good piece of music is produced. Multiple calibrations have been done to amend the parameters of the network for optimal generation. The output is assessed on the basis of rhythm, harmony, and grammar accuracy.