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We describe an inference task in which a set of timestamped eventobservations must be clustered into an unknown number of temporal sequenceswith independent and varying rates of observations. Various existing approachesto multi-object tracking assume a fixed number of sources and/or a fixedobservation rate; we develop an approach to inferring structure in timestampeddata produced by a mixture of an unknown and varying number of similar Markovrenewal processes, plus independent clutter noise. The inference simultaneouslydistinguishes signal from noise as well as clustering signal observations intoseparate source streams. We illustrate the technique via a synthetic experimentas well as an experiment to track a mixture of singing birds.

Segregating event streams and noise with a Markov renewal process model