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.

多重相干是一种频谱分析工具，可以估算多个（可能是部分）相干输入对一个或几个输出的贡献。可以使用瀑布减法（条件光谱分析框架）或输入相关矩阵的特征值分析（虚拟源分析方法）进行此类分析。这些技术在处理聚合的跨谱估计时已经很好地建立了。实际上，由于时间记录的有限性，所以永远不会这样，它可能带来解释问题，尤其是在增加引用数量时。估计一致性的重要性在当前工作中起着核心作用。它涉及基于非相干信号之间的估计相干性的统计行为来执行假设检验。该测试的原理是将低于显着性阈值的估计相干性设为零，在这项工作中扩展到多相干情况。TMC（阈值多重相干性）首先在数字基准的框架中进行说明，然后在实验室风洞测试中进行验证，其中证明了对降噪目的的兴趣。该方法最终应用于在飞行测试期间记录在飞机机舱内部和外部的信号。从外部到内部麦克风使用TMC来分析外部噪声源对内部噪声的影响，或者从内部到外部传感器使用TMC来进行流量噪声抑制。