  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.
