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 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 the engines in realistic flight conditions.

中文翻译：

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使用独立参考消除相关噪声的贝叶斯方法——在超音速喷气噪声提取中的应用

介绍了一种从多通道测量中去除相关噪声的贝叶斯方法。它基于贝叶斯因子分析以及噪声相关结构的先验但不确定的知识。当背景噪声测量可用时，该技术非常适合对在气动声学实验框架中测量的交叉谱矩阵进行去噪，因为它允许将发动机噪声贡献与湍流边界层和均匀噪声分量分开麦克风。使用这种方法对在飞机机身上的嵌入式麦克风上测量的飞行数据进行降噪。结果表明，它对于研究发动机在实际飞行条件下产生的宽带冲击相关噪声具有显着优势。