Iteratively Reweighted Spherical Equivalent Source Method for Acoustic Source Identification

Spherical equivalent source method (S-ESM) using rigid spherical microphone arrays can simultaneously identify sound sources in all directions. In this paper, based on the reweighting and sparse representation frameworks, the sparsity-promoting iteratively reweighted least squares (IRLS) and reweighted ℓ₁-norm minimization (referred as w-ℓ₁-norm) are exploited to improve the performance of acoustic source identification for S-ESM. The numerical and experimental results indicate accurate acoustic source identification for the two iteratively reweighted algorithms. IRLS can provide good acoustic source identification over the wide frequency and measurement distance ranges, improving the performance of the established S-ESM. In addition, w-ℓ₁-norm is also an alternative solution strategy for S-ESM, although at the expense of low computational efficiency and given prior information.

Abstract licence: CC BY-NC-ND

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