An Efficient State-Space Model for Joint Tempo and Meter Tracking

Dynamic Bayesian networks (e.g., Hidden Markov Mod-els) are popular frameworks for meter tracking in music because they are able to incorporate prior knowledge about the dynamics of rhythmic parameters (tempo, meter, rhyth-mic patterns, etc.). One popular example is the bar pointer model, which enables joint inference of these rhythmic pa-rameters from a piece of music. While this allows the mutual dependencies between these parameters to be ex-ploited, it also increases the computational complexity of the models. In this paper, we propose a new state-space discretisation and tempo transition model for this class of models that can act as a drop-in replacement and not only increases the beat and downbeat tracking accuracy, but also reduces time and memory complexity drastically. We in-corporate the new model into two state-of-the-art beat and meter tracking systems, and demonstrate its superiority to the original models on six datasets. 1