Denoising of cross-spectral matrices using canonical coherence

Disturbing noise in microphone signals is an often occurring problem. The present paper has a main focus on measurements with microphone arrays in wind tunnels, where flow noise will be generated in the individual microphones. Considering stationary operational conditions, the cross-spectral matrix (CSM) is first averaged. Subsequently it is applied for aerodynamic noise-source mapping on a vehicle in the flow using one of several possible array processing algorithms. Several methods exist to suppress the flow-noise effects. Long-time averaging will gradually concentrate the flow-noise contributions on the CSM diagonal, and some array processing algorithms can avoid use of the diagonal. Other algorithms need the diagonal. Denoising methods that subtract a maximum of signal power from the diagonal, while retaining all off-diagonal elements unchanged and the matrix positive semidefinite, have been shown to be limited by remaining off-diagonal flow-noise contributions. A couple of algorithms exist that can overcome that limitation by supporting modifications outside the diagonal, but these methods seem to be limited by high computational cost or difficult parameter selection. The present paper introduces a unique canonical-coherence-based method, which is computationally very fast and with automated parameter selection. The performance is investigated through simulated and real measurements.