  The online estimation of rhythmic information, such as beat positions,
downbeat positions, and meter, is critical for many real-time music
applications. Musical rhythm comprises complex hierarchical relationships
across time, rendering its analysis intrinsically challenging and at times
subjective. Furthermore, systems which attempt to estimate rhythmic information
in real-time must be causal and must produce estimates quickly and efficiently.
In this work, we introduce an online system for joint beat, downbeat, and meter
tracking, which utilizes causal convolutional and recurrent layers, followed by
a pair of sequential Monte Carlo particle filters applied during inference. The
proposed system does not need to be primed with a time signature in order to
perform downbeat tracking, and is instead able to estimate meter and adjust the
predictions over time. Additionally, we propose an information gate strategy to
significantly decrease the computational cost of particle filtering during the
inference step, making the system much faster than previous sampling-based
methods. Experiments on the GTZAN dataset, which is unseen during training,
show that the system outperforms various online beat and downbeat tracking
systems and achieves comparable performance to a baseline offline joint method.
