online-causal · heydari2021-beatnet

Offline approaches are typically non-causal, meaning that they make predictions for a given time using data or features associated with a future time. [...] Online approaches are causal, meaning that they operate using only past and present features. These are typically desirable for human-computer interaction (HCI) systems, which must make immediate predictions, like real-time music accompaniment systems. [...] many of them [...] feed a sliding window of data into an offline model to estimate beat positions within upcoming frames. The sliding window strategy has several major drawbacks, including the discontinuity of beat predictions and the need for priming for predictions in the first window, which causes a delay [11]. Some other approaches involve inferring beat positions in real time using multi agent models [...]. 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.