Advanced Processing of Microphone Array Data for Engineering Applications

2008 ◽  
Vol 123 (5) ◽  
pp. 3231-3231
Author(s):  
Sandro Guidati
2013 ◽  
Vol 134 (5) ◽  
pp. 4127-4127 ◽  
Author(s):  
Philip Morris ◽  
Robert Dougherty ◽  
Chris Nelson ◽  
Alan Cain ◽  
Kenneth Brentner

2017 ◽  
Vol 48 (3-4) ◽  
pp. 44-51 ◽  
Author(s):  
Gert Herold ◽  
Ennes Sarradj

The open-source Python library Acoular is aimed at the processing of microphone array data. It features a number of algorithms for acoustic source characterization in time domain and frequency domain. The modular, object-oriented architecture allows for flexible programming and a multitude of applications. This includes the processing of measured array data, the mapping of sources, the filtering of subcomponent noise, and the generation of synthetic data for test purposes. Several examples illustrating its versatility are given, as well as one example for implementing a new algorithm into the package.


2020 ◽  
Vol 68 (6) ◽  
pp. 428-440
Author(s):  
Tim Lübeck ◽  
Hannes Helmholz ◽  
Johannes M. Arend ◽  
Christoph Pörschmann ◽  
Jens Ahrens

2021 ◽  
Vol 11 (2) ◽  
pp. 572
Author(s):  
Weijie Chen ◽  
Luqin Mao ◽  
Kangshen Xiang ◽  
Fan Tong ◽  
Weiyang Qiao

This paper concerns the application of a linear microphone array in the quantitative evaluation of blade trailing-edge (TE) noise reduction. The noise radiation from the blades with straight and serrated TEs is measured in an indoor open-jet wind tunnel. The array data are processed using the inverse method based on the Clean algorithm based on spatial source coherence (Clean-SC). In order to obtain correct application and achieve the best effect for the microphone array test, the computing software for array data reduction is firstly developed and assessed by Sarradj’s benchmark case. The assessment results show that the present array data processing method has a good accuracy with an error less than 0.5 dB in a wide frequency range. Then, a linear array with 32 microphones is designed to identify the noise source of a NACA65(12)-10 blade. The performance of the Clean-SC algorithm is compared with the Clean algorithm based on point spread functions (Clean-PSF) method for experimentally identifying the noise sources of the blade. The results show that there is about a 2 dB error when using the Clean-PSF algorithm due to the interference of different aerodynamic noise sources. Experimental studies are conducted to study the blade TE noise reduction using serrated TEs. The TE noise for the blade with and without sawtooth configurations is measured with the flow speeds from 20 m/s to 70 m/s, and the corresponding Reynolds numbers based on the chord are from 200,000 to 700,000. Parametric studies of the sawtooth amplitude and wavelength are conducted to understand the noise reduction law. It is observed that the TE noise reduction is sensitive to both the amplitude and wavelength. The flow speed also affects the noise reduction in the serrated TEs. To obtain the best noise suppression effect, the sawtooth configuration should be carefully designed according to the actual working conditions and airflow parameters.


Author(s):  
Pieter Sijtsma ◽  
Alice Dinsenmeyer ◽  
Jerome Antoni ◽  
Quentin Leclere
Keyword(s):  

2017 ◽  
Vol 116 ◽  
pp. 50-58 ◽  
Author(s):  
Ennes Sarradj ◽  
Gert Herold

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