# A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

arXiv: https://arxiv.org/abs/1612.05082 · HTML: https://mlsanity.com/paper/1612.05082/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2016.

## Built on by (most cited first, 8 of 8)

- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation](https://mlsanity.com/paper/2509.01588/) (2025)
- [MIRFLEX: Music Information Retrieval Feature Library for Extraction](https://mlsanity.com/paper/2411.00469/) (2024)
- [ChordFormer: A Conformer-Based Architecture for Large-Vocabulary Audio Chord Recognition](https://mlsanity.com/paper/2502.11840/) (2025)
- [Training chord recognition models on artificially generated audio](https://mlsanity.com/paper/2508.05878/) (2025)
- [Transcribing Rhythmic Patterns of the Guitar Track in Polyphonic Music](https://mlsanity.com/paper/2510.05756/) (2025)
- [A Mamba-Based Model for Automatic Chord Recognition](https://mlsanity.com/paper/2601.02101/) (2026)
- [JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings](https://mlsanity.com/paper/2610.15356/) (2026)

## Abstract (arXiv)

Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.