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Tracking multiple audio sources with the von Mises distribution and variational EM

Tracking multiple audio sources with the von Mises distribution and variational EM
Tracking multiple audio sources with the von Mises distribution and variational EM
In this letter, 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.
1070-9908
798-802
Ban, Yutong
3207fee7-1f0d-490e-ace3-736a5fc4c474
Alameda-Pineda, Xavier
346a5a8f-4306-4a71-96e3-b6c76bd49dc8
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Horaud, Radu
86f58721-f833-4200-a8ce-959babf9c522
Ban, Yutong
3207fee7-1f0d-490e-ace3-736a5fc4c474
Alameda-Pineda, Xavier
346a5a8f-4306-4a71-96e3-b6c76bd49dc8
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Horaud, Radu
86f58721-f833-4200-a8ce-959babf9c522

Ban, Yutong, Alameda-Pineda, Xavier, Evers, Christine and Horaud, Radu (2019) Tracking multiple audio sources with the von Mises distribution and variational EM. IEEE Signal Processing Letters, 26 (6), 798-802. (doi:10.1109/LSP.2019.2908376).

Record type: Article

Abstract

In this letter, 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.

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More information

Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 29 March 2019
Published date: 29 March 2019

Identifiers

Local EPrints ID: 438595
URI: http://eprints.soton.ac.uk/id/eprint/438595
ISSN: 1070-9908
PURE UUID: 74ee9798-276e-492e-b7f1-f89c7979e765
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 18 Mar 2020 17:30
Last modified: 19 Mar 2020 01:45

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Contributors

Author: Yutong Ban
Author: Xavier Alameda-Pineda
Author: Christine Evers ORCID iD
Author: Radu Horaud

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