Name
Abstract | ||
|---|---|---|
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 offline 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. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1080/09298210008565462 | JOURNAL OF NEW MUSIC RESEARCH |
Keywords | Field | DocType |
real time,kalman filter | Extended Kalman filter,Fast Kalman filter,Computer science,MIDI,Speech recognition,Moving horizon estimation,Kalman filter,State variable,Dynamical system,Bayesian probability | Conference |
Volume | Issue | ISSN |
29 | 4 | 0929-8215 |
Citations | PageRank | References |
69 | 7.11 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Taylan Cemgil | 1 | 535 | 54.39 |
| Hilbert J. Kappen | 2 | 834 | 103.74 |
| peter desain | 3 | 165 | 31.76 |
| henkjan honing | 4 | 148 | 24.00 |