# End-to-End Musical Key Estimation Using a Convolutional Neural Network

arXiv: https://arxiv.org/abs/1706.02921 · HTML: https://mlsanity.com/paper/1706.02921/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 13 of 13)

- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [Codified audio language modeling learns useful representations for music information retrieval](https://mlsanity.com/paper/2107.05677/) (2021)
- [MARBLE: Music Audio Representation Benchmark for Universal Evaluation](https://mlsanity.com/paper/2306.10548/) (2023)
- [Supervised and Unsupervised Learning of Audio Representations for Music Understanding](https://mlsanity.com/paper/2210.03799/) (2022)
- [LLark: A Multimodal Instruction-Following Language Model for Music](https://mlsanity.com/paper/2310.07160/) (2023)
- [CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following](https://mlsanity.com/paper/2506.12285/) (2025)
- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [On the Effectiveness of Speech Self-supervised Learning for Music](https://mlsanity.com/paper/2307.05161/) (2023)
- [Scaling Up Music Information Retrieval Training with Semi-Supervised Learning](https://mlsanity.com/paper/2310.01353/) (2023)
- [Combolutional Neural Networks](https://mlsanity.com/paper/2507.21202/) (2025)
- [Linear Complexity Self-Supervised Learning for Music Understanding with Random Quantizer](https://mlsanity.com/paper/2601.09603/) (2026)
- [Masked Contrastive Pre-Training Improves Music Audio Key Detection](https://mlsanity.com/paper/2604.10021/) (2026)

## Abstract (arXiv)

We present an end-to-end system for musical key estimation, based on a convolutional neural network. The proposed system not only out-performs existing key estimation methods proposed in the academic literature; it is also capable of learning a unified model for diverse musical genres that performs comparably to existing systems specialised for specific genres. Our experiments confirm that different genres do differ in their interpretation of tonality, and thus a system tuned e.g. for pop music performs subpar on pieces of electronic music. They also reveal that such cross-genre setups evoke specific types of error (predicting the relative or parallel minor). However, using the data-driven approach proposed in this paper, we can train models that deal with multiple musical styles adequately, and without major losses in accuracy.