Don't look back: an online beat tracking method using RNN and enhanced particle filtering
This addresses the challenge of real-time beat tracking for applications requiring immediate response, though it appears incremental as it builds on existing RNN and particle filtering techniques.
The paper tackles the problem of online beat tracking (OBT) by proposing a method that uses a unidirectional RNN and enhanced particle filtering to infer beat positions from current time frame activations only, achieving significant accuracy improvements over state-of-the-art OBT methods and performance similar to offline methods.
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.