Three-dimensional source localization using sparse Bayesian learning on a spherical microphone array

Guoli Ping, Efren Fernandez Grande, Peter Gerstoft, Zhigang Chu

    Research output: Contribution to journalJournal articleResearchpeer-review


    The identification of acoustic sources in a three-dimensional (3D) domain based on measurements with an array of microphones is a challenging problem: it entails the estimation of the angular position of the sources (direction of arrival), distance relative to the array (range), and the quantification of the source amplitudes. A 3D source localization model using a rigid spherical microphone array with spherical wave propagation is proposed. In this study, sparse Bayesian learning is used to perform localization in 3D space and examine the use of principal component analysis to denoise the measurement data. The performance of the proposed method is examined numerically and experimentally, which is tested both in a free-field and in a reverberant environment. The numerical and experimental investigations demonstrate that the approach offers accurate localization in a 3D domain, resolving closely spaced sources and making it possible to identify sources located at different ranges.
    Original languageEnglish
    JournalJournal of the Acoustical Society of America
    Issue number6
    Pages (from-to)3895-3904
    Publication statusPublished - 2020


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