Tempo tracking and rhythm quantization by sequential Monte Carlo

The structure of the proposed model is equivalent to a switching state space model. We formulate twowell known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering andmaximum a posteriori (MAP) state estimation tasks. The inferences are carried out using sequential Monte Carlo integration (particlefiltering) techniques. Music notation can be viewed as a list of the pitch levels and corresponding timestamps. Ideally, one would like to recover a score directly frOID: sound.