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Iterative beamforming for identification of multiple broadband sound sources

Abstract : The reconstruction of broadband sound sources is an important issue in industrial acoustics. In this paper, a model comprising multiple incoherent Gaussian random sources is considered. The aim is to estimate locations and powers of the sound sources using the pressures measured by an array of microphones. Each measured pressure is interpreted as a mixture of latent signals emitted by different sound sources. Then, an Iterative Beamforming (IB) method is developed to estimate the source parameters. This approach is based on the Expectation-Maximization (EM) algorithm, a well-known iterative procedure for solving maximum likelihood parameter estimation. More specifically, IB iteratively estimates the source contributions and performs beamforming on these estimates. In this work, experiments on real data illustrate the advantage of IB with respect to classical beamforming and Near-field Acoustical Holography (NAH). In particular, the proposed method is shown to work over a wider range of frequencies, to better estimate the source locations, and is able to quantify the powers of the sources. Furthermore, experiments illustrate that IB can not only localize the sources on a given surface, but also accurately estimate their 3D locations.
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Submitted on : Monday, December 28, 2015 - 12:07:34 PM
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Xun Wang, Benjamin Quost, Jean-Daniel Chazot, Jérôme Antoni. Iterative beamforming for identification of multiple broadband sound sources. Journal of Sound and Vibration, Elsevier, 2016, 365, pp.260-275. ⟨10.1016/j.jsv.2015.11.036⟩. ⟨hal-01248734⟩



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