Tracking Multiple Audio Sources with the von Mises Distribution and Variational EM
This addresses the challenge of real-time audio source tracking for robotics and audio processing applications, but it is incremental as it builds on existing Bayesian filtering methods with a novel approximation.
The paper tackles the problem of tracking multiple moving audio sources by estimating their trajectories from observed features, using a von Mises distribution and variational EM, achieving computational tractability and time efficiency in experiments with the LOCATA dataset.
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.