A Noise-Robust Method with Smoothed \ell_1/\ell_2 Regularization for Sparse Moving-Source Mapping
Data Analysis, Statistics and Probability
2016-04-13 v1 Information Theory
math.IT
Abstract
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.
Cite
@article{arxiv.1604.03450,
title = {A Noise-Robust Method with Smoothed \ell_1/\ell_2 Regularization for Sparse Moving-Source Mapping},
author = {Mai Quyen Pham and Benoit Oudompheng and Jérôme I. Mars and Barbara Nicolas},
journal= {arXiv preprint arXiv:1604.03450},
year = {2016}
}