  A method is presented for the rhythmic parsing problem: Given a sequence of
observed musical note onset times, we estimate the corresponding notated rhythm
and tempo process. A graphical model is developed that represents the
simultaneous evolution of tempo and rhythm and relates these hidden quantities
to observations. The rhythm variables are discrete and the tempo and
observation variables are continuous. We show how to compute the globally most
likely configuration of the tempo and rhythm variables given an observation of
note onset times. Preliminary experiments are presented on a small data set. A
generalization to arbitrary conditional Gaussian distributions is outlined.
