# Genre-Agnostic Key Classification With Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1808.05340 · HTML: https://mlsanity.com/paper/1808.05340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

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

- [SingSong: Generating musical accompaniments from singing](https://mlsanity.com/paper/2301.12662/) (2023)
- [Masked Contrastive Pre-Training Improves Music Audio Key Detection](https://mlsanity.com/paper/2604.10021/) (2026)

## Abstract (arXiv)

We propose modifications to the model structure and training procedure to a recently introduced Convolutional Neural Network for musical key classification. These modifications enable the network to learn a genre-independent model that performs better than models trained for specific music styles, which has not been the case in existing work. We analyse this generalisation capability on three datasets comprising distinct genres. We then evaluate the model on a number of unseen data sets, and show its superior performance compared to the state of the art. Finally, we investigate the model's performance on short excerpts of audio. From these experiments, we conclude that models need to consider the harmonic coherence of the whole piece when classifying the local key of short segments of audio.