Instantaneous Bayesian regularization applied to real-time near-field acoustic holography

Abstract

Real-time near-field acoustic holography (RT-NAH) is used to recover non-stationary sound sources using a planar microphone array. Direct propagation is described by the convolution of the wavenumber spectrum of the source under study with a known impulse response. The deconvolution operation is achieved by a singular value decomposition of the propagator and Tikhonov regularization is performed to stabilize the solution. The inverse problem has an innate ill-posed characteristic, and the regularization process is the key factor in obtaining acceptable results. The purpose of this paper is to present the instantaneous regularization process applied to RT-NAH method. Bayesian estimation of the regularization parameter is introduced from prior knowledge of the problem. The computation of the regularization parameter is updated for each block of constant time interval allowing one to take into account the fluctuating properties of the sound field. The superiority of Bayesian regularization, compared to state-of-the art methods, is observed numerically and experimentally for reconstruction of non-stationary sources. RT-NAH is also enhanced to allow the reconstruction of long signals. Updating the regularization parameter accordingly to the fluctuations of the SNR is revealed to be a necessary effort to reconstruct highly non-stationary sources.