On tempo tracking: Tempogram representation and Kalman filtering
On tempo tracking: Tempogram representation and Kalman filtering
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DOI：
10.1080/09298210008565462
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发表时间：
2000-12-01
影响因子：
1.1
通讯作者：
Honing, H
中科院分区：
文献类型：
--
作者：
Cemgil, AT;Kappen, B;Honing, H
我们制定克里思跟踪贝叶斯框架中的克里思跟踪器建模为一个随机动态系统。克里思被建模为一个隐藏的状态变量的系统，并估计由卡尔曼滤波器。卡尔曼滤波器的操作上的Tempogram，小波样的多尺度扩展的真实的性能。我们的方法的一个重要优点是，它是可能制定离线或实时算法。对披头士乐队的《昨天》和《米歇尔》的一组系统收集的钢琴演奏的模拟结果显示，大约90%的节拍被准确地跟踪。
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.