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

TL;DR

The paper introduces DLB, an efficient online beat tracking method using RNN activations and enhanced particle filtering, providing immediate and accurate beat detection without needing past data initialization.

Contribution

It presents a novel real-time beat tracking approach that eliminates the need for past data, enabling immediate response and improved accuracy over existing online methods.

Findings

DLB achieves beat tracking accuracy comparable to offline methods.

It provides immediate beat detection without initial data priming.

The method outperforms state-of-the-art online beat tracking techniques.

Abstract

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.