On tempo tracking: Tempogram Representation and Kalman filtering
Ali Taylan Cemgil, Bert Kappen, Peter Desain, Henkjan Honing · Journal of New Music Research · 2000
We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated by a Kalman filter. The Kalman filter operates on a Tempogram, a wavelet-like multiscale expansion of a real performance. An important advantage of our approach is that it is possible to formulate both off-line or real-time algorithms. The simulation results on a systematically collected set of MIDI piano performances of Yesterday and Michelle by the Beatles shows accurate tracking of approximately 90% of the beats.