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Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms

Abstract : This paper considers the problem of identifying multiple sound sources from acoustical measurements obtained by an array of microphones. The problem is solved via maximum likelihood. In particular, an expectation-maximization (EM) approach is used to estimate the sound source locations and strengths, the pressure measured by a microphone being interpreted as a mixture of latent signals emitted by the sources. This work also considers two kinds of uncertainties pervading the sound propagation and measurement process: uncertain microphone locations and uncertain wavenumber. These uncertainties are transposed to the data in the belief functions framework. Then, the source locations and strengths can be estimated using a variant of the EM algorithm, known as the Evidential EM (E2M) algorithm. Eventually, both simulation and real experiments are shown to illustrate the advantage of using the EM in the case without uncertainty and the E2M in the case of uncertain measurement.
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https://hal.archives-ouvertes.fr/hal-01248727
Contributor : Benjamin Quost <>
Submitted on : Monday, December 28, 2015 - 11:46:04 AM
Last modification on : Wednesday, July 8, 2020 - 12:42:26 PM

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Xun Wang, Benjamin Quost, Jean-Daniel Chazot, Jérôme Antoni. Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms. Mechanical Systems and Signal Processing, Elsevier, 2016, 66-67, pp.159-177. ⟨10.1016/j.ymssp.2015.06.011⟩. ⟨hal-01248727⟩

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