Spectral estimation method for noisy data using a noise reference

Abstract

The development of array processing methods to extract the useful characteristics of acoustic sources such as their locations and absolute levels, starting from the measured sound field is one of the main issues in aero-acoustics. Generally, the methods are based on a deconvolution operation to remove the undesirable effects of smearing produced by array response. This process should be carried out after the additive noise has been suitably attenuated and, ideally, the deconvolution operator should amplify the noise as little as possible. We show that when a reference of noise is known beforehand, and under certain assumptions, that it is possible both to remove the smearing effect produced by array response and to reduce the noise contamination of the results using a method called Spectral Estimation Method With Additive Noise. This method has been applied to computer and experimental simulations involving acoustic sources radiating in a noisy environment. The levels of the sources were found with a good accuracy and the background noise highly reduced, confirming the validity of the approach and the good performance of the proposed method.

A robust super-resolution approach with sparsity constraint in acoustic imaging

“…Recently, many effective methods have been proposed to solve Eq. (11), such as the DAMAS with sparsity constraint (SC-DAMAS) [9]. But β selection is the key issue for good performance.…”

“…The sparsity constraint is interpreted by the total source power β so as to regularize data fitting errors in Eq. (11) and Eq. 13 data y 1 and y 2 respectively in the case of very strong background noises, it is highly necessary to investigate other sparsity prior models [19,33,37] instead of only using sparsity constraint β = 6.…”

“…The Covariance Matrix Fitting (CMF) method [10] can effectively improve the robustness by jointly estimating the source power covariance matrix and background noise power; however, the original CMF is not feasible to use because of its huge dimensionality of variables in covariance matrix. For robust acoustic imaging, the Spectral Estimation Method (SEM) and its extensions [11,12] are proposed to subtract the reference noise power from the measured data; and this reference noise power can be obtained beforehand by measuring the observed signals without any object in wind tunnel. However, the estimated noise power might be different from the case where the object is installed in the wind tunnel.…”

A review of acoustic imaging methods using phased microphone arrays

“…In general, measurements with phased microphone arrays provide certain advantages with respect to measurements with individual microphones when performing acoustic measurements. For experiments in wind tunnels with openand closed-test sections, as well as in engine test cells, the background noise suppression capability of the source localization algorithms is very useful [1,[16][17][18], as well as the removal of reflections from the walls [19,20]. Beamforming can also be applied to moving objects, such as flying aircraft or rotating blades, provided that the motion of the source is tracked accurately [1].…”

Comparison of microphone array denoising techniques and application to flight test measurements

“…In this context, multiple denoising processes have been developed, based on various strategies such as Principal Component Analysis [1], wavenumber filtering [2], diagonal optimization [3,4] and some methods also make use of a background noise measurement [5][6][7]. Another common practice is to set the diagonal of the measured cross-spectral matrix (CSM) to zero.…”