# Don't look back: an online beat tracking method using RNN and enhanced particle filtering

arXiv: https://arxiv.org/abs/2011.02619 · HTML: https://mlsanity.com/paper/2011.02619/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Audio Flamingo Next: Next-Generation Open Audio-Language Models for Speech, Sound, and Music](https://mlsanity.com/paper/2604.10905/) (2026)
- [BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking](https://mlsanity.com/paper/2108.03576/) (2021)
- [Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies](https://mlsanity.com/paper/2210.15559/) (2022)

## Abstract (arXiv)

Online beat tracking (OBT) has always been a challenging task. Due to the inaccessibility of future data and the need to make inference in real-time. We propose Do not Look back! (DLB), a novel approach optimized for efficiency when performing OBT. DLB feeds the activations of a unidirectional RNN into an enhanced Monte-Carlo localization model to infer beat positions. Most preexisting OBT methods either apply some offline approaches to a moving window containing past data to make predictions about future beat positions or must be primed with past data at startup to initialize. Meanwhile, our proposed method only uses activation of the current time frame to infer beat positions. As such, without waiting at the beginning to receive a chunk, it provides an immediate beat tracking response, which is critical for many OBT applications. DLB significantly improves beat tracking accuracy over state-of-the-art OBT methods, yielding a similar performance to offline methods.