On tempo tracking: Tempogram representation and Kalman filtering
SourceJournal of New Music Research, 29, 4, (2001), pp. 259-273
Article / Letter to editor
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SW OZ DCC AI
Journal of New Music Research
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.
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