# On the Typicality of Musical Sequences

arXiv: https://arxiv.org/abs/2211.13016 · HTML: https://mlsanity.com/paper/2211.13016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [This Time with Feeling: Learning Expressive Musical Performance](https://mlsanity.com/paper/1808.03715/) (2018)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [On the probability-quality paradox in language generation](https://mlsanity.com/paper/2203.17217/) (2022)

## Built on by (most cited first, 2 of 2)

- [Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model](https://mlsanity.com/paper/2308.09454/) (2023)
- [Estimating Musical Surprisal in Audio](https://mlsanity.com/paper/2501.07474/) (2025)

## Abstract (arXiv)

It has been shown in a recent publication that words in human-produced English language tend to have an information content close to the conditional entropy. In this paper, we show that the same is true for events in human-produced monophonic musical sequences. We also show how "typical sampling" influences the distribution of information around the entropy for single events and sequences.