  In this paper we address the problem of simultaneously tracking several
moving audio sources, namely the problem of estimating source trajectories from
a sequence of observed features. We propose to use the von Mises distribution
to model audio-source directions of arrival with circular random variables.
This leads to a Bayesian filtering formulation which is intractable because of
the combinatorial explosion of associating observed variables with latent
variables, over time. We propose a variational approximation of the filtering
distribution. We infer a variational expectation-maximization algorithm that is
both computationally tractable and time efficient. We propose an audio-source
birth method that favors smooth source trajectories and which is used both to
initialize the number of active sources and to detect new sources. We perform
experiments with the recently released LOCATA dataset comprising two moving
sources and a moving microphone array mounted onto a robot.
