Abstract
We consider the problem of sound sources localization from acoustical measurements obtained from a set of microphones. We formalize the problem within a statistical framework: the pressure measured by a microphone is interpreted as a mixture of the signals emitted by the sources, pervaded by a Gaussian noise. Maximum-likelihood estimates of the parameters of the model (locations and strengths of the sources) may then be computed via the EM algorithm. In this work, we introduce two sources of uncertainties: the location of the microphones and the wavenumber. First, we show how these uncertainties may be transposed to the data using belief functions. Then, we detail how the localization problem may be studied using a variant of the EM algorithm, known as Evidential EM algorithm. Eventually, we present simulation experiments which illustrate the advantage of using the Evidential EM algorithm when uncertain data are available.
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Wang, X., Quost, B., Chazot, JD., Antoni, J. (2013). Sound Source Localization from Uncertain Information Using the Evidential EM Algorithm. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds) Scalable Uncertainty Management. SUM 2013. Lecture Notes in Computer Science(), vol 8078. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40381-1_13
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DOI: https://doi.org/10.1007/978-3-642-40381-1_13
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