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The online estimation of rhythmic information, such as beat positions,downbeat positions, and meter, is critical for many real-time musicapplications. Musical rhythm comprises complex hierarchical relationshipsacross time, rendering its analysis intrinsically challenging and at timessubjective. Furthermore, systems which attempt to estimate rhythmic informationin 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 metertracking, which utilizes causal convolutional and recurrent layers, followed bya pair of sequential Monte Carlo particle filters applied during inference. Theproposed system does not need to be primed with a time signature in order toperform downbeat tracking, and is instead able to estimate meter and adjust thepredictions over time. Additionally, we propose an information gate strategy tosignificantly decrease the computational cost of particle filtering during theinference step, making the system much faster than previous sampling-basedmethods. Experiments on the GTZAN dataset, which is unseen during training,show that the system outperforms various online beat and downbeat trackingsystems and achieves comparable performance to a baseline offline joint method.

BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking