# Robust Downbeat Tracking Using an Ensemble of Convolutional Networks

arXiv: https://arxiv.org/abs/1605.08396 · HTML: https://mlsanity.com/paper/1605.08396/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2016.

## Built on by (most cited first, 6 of 6)

- [BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking](https://mlsanity.com/paper/2108.03576/) (2021)
- [Cue Point Estimation using Object Detection](https://mlsanity.com/paper/2407.06823/) (2024)
- [Drum-Aware Ensemble Architecture for Improved Joint Musical Beat and Downbeat Tracking](https://mlsanity.com/paper/2106.08685/) (2021)
- [Source Separation-based Data Augmentation for Improved Joint Beat and Downbeat Tracking](https://mlsanity.com/paper/2106.08703/) (2021)
- [Learning Hierarchical Metrical Structure Beyond Measures](https://mlsanity.com/paper/2209.10259/) (2022)
- [JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings](https://mlsanity.com/paper/2610.15356/) (2026)

## Abstract (arXiv)

In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm and bass content to feed convolutional neural networks that are adapted to take advantage of each feature characteristics. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the metrical continuity of a song. We then perform an evaluation of our system on a large base of 9 datasets, compare its performance to 4 other published algorithms and obtain a significant increase of 16.8 percent points compared to the second best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.