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Beamforming matrix regularization and inverse problem for sound source localization: Application to aero-engine noise

机译:波束成形矩阵正则化和声源定位反问题:在航空发动机噪声中的应用

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Phased-microphone arrays associated with beamforming have become a standard technique to localize aeroacoustic sources. The limitations of beamforming have been overcome thanks to deconvolution technique (DAMAS or CLEAN-SC) or iterative process (L1-GIB). However, the computational cost of these methods can be large or assumptions on the source coherence have to be done. In this paper we present a technique based on inverse methods initially developed for sound field extrapolation. The aim is to use a beamforming regularization matrix to penalize the non-signal region in the inverse problem. First, this Hybrid Method is applied to laboratory experiments to demonstrate its effectiveness. Then noise data of an aero-engine measured over a half circular, far-field microphone array are used. The source maps obtained show that the Hybrid Method provides better spatial resolution than beamforming, similar to Clean-SC and results in less iterations of DAMAS.
机译:与波束成形相关的相控麦克风阵列已经成为定位空气声源的标准技术。借助反卷积技术(DAMAS或CLEAN-SC)或迭代过程(L1-GIB),克服了波束成形的局限性。但是,这些方法的计算成本可能很大,或者必须对源一致性进行假设。在本文中,我们提出了一种基于逆方法的技术,该方法最初是为声场外推而开发的。目的是使用波束成形正则化矩阵对逆问题中的非信号区域进行惩罚。首先,将此混合方法应用于实验室实验以证明其有效性。然后,使用在半圆形远场麦克风阵列上测得的航空发动机的噪声数据。获得的源图显示,与Clean-SC相似,Hybrid方法提供的空间分辨率比波束形成更好,并且DAMAS的迭代次数更少。

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