Sequential Inference of Rhythmic Structure in Musical Audio

This paper presents a framework for the modelling of temporal characteristics of musical signals and an approximate, sequential Monte Carlo inference scheme which yields estimates of tempo and rhythmic pattern from onset-time data. These two features are quantified through the construction of a probabilistic dynamical model of a hidden 'bar-pointer' and a Poisson observation model. The capabilities of the system are demonstrated by tracking the tempo of a 2 against 3 polyrhythm and detecting a switch in rhythm in a MIDI performance.