Conventionally, it has been done by ``quantization'' of IOIs (Inter-Onset Intervals) of played notes. We used HMM (Hidden Markov Model) to solve this problem (Saito et al. 1999) by modeling both the fluctuating note lengths and the probabilistic constraint of note sequences. In this work, we also included multiple tempos in the HMM to find the best-matching tempo. In other works, tempo was included as hidden variables of probabilistic models (Cemgil et al, 2000; Raphael, 2001), or determined by clustering IOIs (Dixon, 2001), and rhythm was estimated based on the tempo.
In this paper, we treat rhythm recognition as a problem of probabilistically decomposing the observed IOIs into rhythm and tempo components.