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The multiple coherence is a spectral analysis tool allowing the estimation of the contribution of several, possibly partially, coherent inputs to one or several outputs. This type of analysis can be conducted using a waterfall substraction approach (Conditioned Spectral Analysis framework) or using an eigenvalue analysis of the input correlation matrix (Virtual Source Analysis approaches). Those techniques are well established when dealing with converged cross-spectral estimates. In practice, this is never the case because of the finite nature of time records, and it can bring interpretation issues, particularly when increasing the number of references. The significance of the estimated coherence plays a central role in the present work. It involves the implementation of an hypothesis test based upon the statistical behavior of the estimated coherence between incoherent signals. This test, whose principle is to put to zero an estimated coherence that is below a significance threshold, is extended in this work to the multiple coherence case. The TMC (Thresholded Multiple Coherence) is first illustrated in the frame of a numerical benchmark, and then validated in a laboratory wind tunnel test where the interest for denoising purpose is demonstrated. The approach is finally applied to signals recorded inside and outside the cabin of an aircraft during a flight test. The TMC is used either from outside to inside microphones, to analyse the contribution of outside noise sources to the interior noise, or alternatively from inside to outside sensors, for flow noise rejection purpose.

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This paper deals with the denoising of microphone array measurements. In many situations, flush mounted microphone arrays are polluted by a turbulent boundary layer, this is typically the case considering wind tunnels or inflight tests. Acoustic imaging results are strongly affected by this noise, classical approaches to solve this issue consist in removing the diagonal terms from the measured cross spectral matrix, or to implement background noise subtraction strategies. This can be sufficient for conventional beamforming approaches, but can be a limitation when implementing more advanced identification methods. This work introduces two alternative techniques, a first one based on a statistical model whose parameters are inferred from measurements (PFA-Probabilistic Factor Analysis), and a second one based on the use of noise-free references. The former is an original contribution of the work, while the later is a well known approach yet not often used in the present context. Both methods, as well as more classical approaches, are compared in the frame of inflight array measurements for the characterization of jet noise. It is shown that the proposed advanced denoising approaches show enhanced performances as compared to classical approaches when applying either conventional beamforming or inverse source characterization.