Seminar by Nicolas Aujogue

In this seminar, a Bayesian method to remove correlated noise from multi-channel measurements is introduced. It is based on Bayesian factor analysis coupled with prior but uncertain knowledge of the correlation structure of the noise. This technique is particularly well suited to denoise cross-spectral matrices measured in the frame of aeroacoustic experiments when background noise measurements are available, because it allows separating the engine noise contribution from the turbulent boundary layer and uniform noise components that are all sensed by in-flow microphones. In-flight data measured on flush-mounted microphones on an aircraft fuselage are denoised using  this method. It is shown that it has a significant benefit for studying the broadband shock-associated noise generated by full-scale dual-stream engines in realistic flight conditions. A first-order physical analysis of the identified acoustic signature is proposed to link the sound-generating aerodynamic mechanisms with the measured acoustic field.