# A Large-Scale Study of Language Models for Chord Prediction

arXiv: https://arxiv.org/abs/1804.01849 · HTML: https://mlsanity.com/paper/1804.01849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions](https://mlsanity.com/paper/2410.22046/) (2024)

## Abstract (arXiv)

We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically explore hyper-parameter settings for the recurrent neural networks---a crucial step in achieving good results with this model class. Our results show not only a quantitative difference between the models, but also a qualitative one: in contrast to static N-gram models, certain RNN configurations adapt to the songs at test time. This finding constitutes a further step towards the development of chord recognition systems that are more aware of local musical context than what was previously possible.