The method described here performs blind deconvolution of the beamforming
output in the frequency domain. To provide accurate blind deconvolution,
sparsity priors are introduced with a smooth \ell_1/\ell_2 regularization term.
As the mean of the noise in the power spectrum domain is dependent on its
variance in the time domain, the proposed method includes a variance estimation
step, which allows more robust blind deconvolution. Validation of the method on
both simulated and real data, and of its performance, are compared with two
well-known methods from the literature: the deconvolution approach for the
mapping of acoustic sources, and sound density modeling

Mars, Jérôme I.

Nicolas, Barbara

Oudompheng, Benoit

Pham, Mai Quyen

English

arXiv

International audienceThe method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smoothed 1 // 2 regularization term. As the mean of the noise in the power spectrum domain depends on its variance in the time domain, the proposed method includes a variance estimation step, which allows more robust blind deconvolution. Validation of the method on both simulated and real data, and of its performance, are compared with two well-known methods from the literature: the deconvolution approach for the mapping of acoustic sources, and sound density modeling

Pham, Mai Quyen,

Oudompheng, Benoit,

Mars, Jerome,

HAL Descartes

A Noise-Robust Method with Smoothed L1/L 2 Regularization for Sparse Moving-Source Mapping

Pham, Mai Quyen, I

Oudompheng, Benoit, I

Mars, Jerome, I.

HAL-UJM

Hal - Université Grenoble Alpes

HAL-Inserm

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