Mapping uncertainties involved in sound source reconstruction with a cross-spectral-matrix-based Gibbs sampler.

The reconstruction of sound sources by using inverse methods is known to be prone to estimation errors due to measurement noise, model mismatch, and poor conditioning of the inverse problem. This paper introduces a solution to map the estimation errors together with the reconstructed sound sources. From a Bayesian perspective, it initializes a Gibbs sampler with the Bayesian focusing method.

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

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.