  In musical performances with expressive tempo modulation, the tempo variation
can be modelled as a sequence of tempo arcs. Previous authors have used this
idea to estimate series of piecewise arc segments from data. In this paper we
describe a probabilistic model for a time-series process of this nature, and
use this to perform inference of single- and multi-level arc processes from
data. We describe an efficient Viterbi-like process for MAP inference of arcs.
Our approach is score-agnostic, and together with efficient inference allows
for online analysis of performances including improvisations, and can predict
immediate future tempo trajectories.
