Low-frequency wind noise correlation in microphone arrays

2005 ◽  
Vol 117 (6) ◽  
pp. 3489-3496 ◽  
Author(s):  
F. Douglas Shields
Keyword(s):  
Low Frequency ◽  
Microphone Arrays ◽  
Noise Correlation ◽  
Wind Noise

scholarly journals Acoustic source localization using the open spherical microphone array in the low-frequency range

2019 ◽  
Vol 283 ◽  
pp. 04001
Author(s):  
Boquan Yang ◽  
Shengguo Shi ◽  
Desen Yang
Keyword(s):  
Source Localization ◽  
Generalized Inverse ◽  
Microphone Array ◽  
Low Frequency ◽  
Poor Performance ◽  
Microphone Arrays ◽  
Three Dimension ◽  
Frequency Range ◽  
Array Aperture ◽  
Regularization Matrix

Recently, spherical microphone arrays (SMA) have become increasingly significant for source localization and identification in three dimension due to its spherical symmetry. However, conventional Spherical Harmonic Beamforming (SHB) based on SMA has limitations, such as poor resolution and high side-lobe levels in image maps. To overcome these limitations, this paper employs the iterative generalized inverse beamforming algorithm with a virtual extrapolated open spherical microphone array. The sidelobes can be suppressed and the main-lobe can be narrowed by introducing the two iteration processes into the generalized inverse beamforming (GIB) algorithm. The instability caused by uncertainties in actual measurements, such as measurement noise and configuration problems in the process of GIB, can be minimized by iteratively redefining the form of regularization matrix and the corresponding GIB localization results. In addition, the poor performance of microphone arrays in the low-frequency range due to the array aperture can be improved by using a virtual extrapolated open spherical array (EA), which has a larger array aperture. The virtual array is obtained by a kind of data preprocessing method through the regularization matrix algorithm. Both results from simulations and experiments show the feasibility and accuracy of the method.

Low frequency wind noise contributions in measurement microphones

2008 ◽  
Vol 123 (3) ◽  
pp. 1260-1269 ◽  
Author(s):  
Richard Raspet ◽  
Jiao Yu ◽  
Jeremy Webster
Keyword(s):  
Low Frequency ◽  
Wind Noise

Spatial structure of low-frequency wind noise

2007 ◽  
Vol 122 (6) ◽  
pp. EL223-EL228 ◽  
Author(s):  
D. Keith Wilson ◽  
Roy J. Greenfield ◽  
Michael J. White
Keyword(s):  
Spatial Structure ◽  
Low Frequency ◽  
Wind Noise

scholarly journals Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Zhang ◽  
Enliang Song ◽  
JingChang Huang ◽  
Huawei Liu ◽  
YuePeng Wang ◽  
...  
Keyword(s):  
Microphone Arrays ◽  
Portable Devices ◽  
Frequency Error ◽  
Acoustic Source ◽  
Wind Noise ◽  
Small Aperture ◽  
Acoustic Source Localization ◽  
Mems Microphone ◽  
Localization Performance ◽  
Array Aperture

Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold). Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.

Wind Noise Reduction for Outdoor Measurement Microphones With Windscreens

2008 ◽  
Author(s):  
Z. C. Zheng ◽  
Ying Xu
Keyword(s):  
Noise Reduction ◽  
High Frequency ◽  
Low Frequency ◽  
High Flow ◽  
Low Flow ◽  
Computational Techniques ◽  
Wind Noise ◽  
Flow Resistivity ◽  
Materials Used ◽  
Wind Noise Reduction

In this study, effects of windscreen material property on wind noise reduction are investigated at different frequencies of incoming wind turbulence. The properties of porous materials used for the windscreen are represented by flow resistivity. Computational techniques are developed to study the detailed flow around the windscreen as well as flow inside the windscreen that uses a porous material as the medium. The coupled simulation shows that for low-frequency turbulence, the windscreens with low flow resistivity are more effective in noise reduction. Contrarily, for high-frequency turbulence, the windscreens with high flow resistivity are more effective.

Low‐frequency wind noise reduction by spherical windscreens

2007 ◽  
Vol 121 (5) ◽  
pp. 3063-3063 ◽  
Author(s):  
Richard Raspet ◽  
Jeremy Webster ◽  
Jiao Yu
Keyword(s):  
Noise Reduction ◽  
Low Frequency ◽  
Wind Noise ◽  
Wind Noise Reduction
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