Tracking Multiple Audio Sources with the von Mises Distribution and Variational EM

Yutong Ban 1 Xavier Alameda-Pineda 1 Christine Evers 2 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper, we address the problem of simultaneously tracking several audio sources, namely the problem of estimating source trajectories from a sequence of the observed features. We propose to use the von Mises distribution to model audio-source directions of arrival (DOAs) with circular random variables. This leads to a multi-target Kalman filter formulation which is intractable because of the combinatorial explosion of associating observations to state variables over time. We propose a variational approximation of the filter's posterior distribution and we infer a variational expectation maximization (VEM) algorithm which is computationally efficient. We also 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 a recently released dataset comprising several moving sources as well as a moving microphone array.
Type de document :
Pré-publication, Document de travail
Paper submitted to IEEE Signal Processing Letters. 2018
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https://hal.inria.fr/hal-01969050
Contributeur : Team Perception <>
Soumis le : jeudi 3 janvier 2019 - 15:57:03
Dernière modification le : vendredi 4 janvier 2019 - 17:07:01

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  • HAL Id : hal-01969050, version 1
  • ARXIV : 1812.08246

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Yutong Ban, Xavier Alameda-Pineda, Christine Evers, Radu Horaud. Tracking Multiple Audio Sources with the von Mises Distribution and Variational EM. Paper submitted to IEEE Signal Processing Letters. 2018. 〈hal-01969050〉

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