A Dynamical Bayesian Network for Tempo and Polyphonic Pitch Tracking
We present a model for simultaneous tempo and polyphonic pitch tracking. Both these acoustic analysis tasks are difficult and, arguably, no satisfactory solution currently exists for polyphonic pitch tracking. Our model, a form of Dynamical Bayesian Network, embodies a transparent and computationally tractable approach to this acoustic analysis problem. An advantage of our approach is that it places emphasis on modelling the sound generation procedure. It provides a clear framework in which both high level (cognitive) prior information on music structure can be coupled with low level (acoustic physical) information in a principled manner to perform the analysis. The model is readily extensible to more complex sound generation pro-