Automated Rhythm Transcription

We present a technique that, given a sequence of musical note onset times, performs simultaneous identi cation of the notated rhythm and the variable tempo associated with the times. Our formulation is probabilistic: We develop a stochastic model for the interconnected evolution of a rhythm process, a tempo process, and an observable process. This model allows the globally optimal identi cation of the most likely rhythm and tempo sequence, given the observed onset times. We demonstrate applications to a sequence of times derived from a sampled audio le and to MIDI data.

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