scholarly journals Noise source identification by using near field acoustic holograpy and focused beamforming based on spherical microphone array with random unifrom distribution of elements

2017 ◽  
Vol 66 (1) ◽  
pp. 014303
Author(s):  
Zhang Lan-Yue ◽  
Ding Dan-Dan ◽  
Yang De-Sen ◽  
Shi Sheng-Guo ◽  
Zhu Zhong-Rui
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Near Field ◽  
Distribution Of Elements ◽  
Noise Source Identification

Aircraft Noise Source Identification Using a Microphone Array: Montreal-Trudeau Airport Test Campaign

2013 ◽  
Author(s):  
Robby Lapointe ◽  
Alain Berry ◽  
Cédric Camier ◽  
Jean-Francois Blais ◽  
Mathieu Patenaude-Dufour ◽  
...  
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Aircraft Noise ◽  
Noise Source Identification

Industrial Noise Source Identification by Using an Acoustic Beamforming System

1998 ◽  
Vol 120 (2) ◽  
pp. 426-433 ◽  
Author(s):  
M. R. Bai ◽  
J. Lee
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Field Tests ◽  
Minimum Variance ◽  
Industrial Applications ◽  
Noise Sources ◽  
Multiple Signal Classification ◽  
Noise Source Identification ◽  
Identification Technique

A noise source identification technique is proposed for industrial applications by using a microphone array and beamforming algorithms. Both of the directions and the distances of long-range noise sources are calculated. The conventional method, the minimum variance (MV) method, and the multiple signal classification (MUSIC) method are the main beamforming algorithms employed in this study. The results of numerical simulations and field tests indicate the effectiveness of the acoustic beam-former in identifying noise sources in industrial environments.

Noise-source identification of a high-speed train by noise source level analysis

2016 ◽  
Vol 231 (6) ◽  
pp. 717-728 ◽  
Author(s):  
Hee-Min Noh
Keyword(s):  
Source Identification ◽  
High Speed ◽  
Sound Pressure ◽  
Noise Source ◽  
Microphone Array ◽  
Beam Power ◽  
High Speed Train ◽  
Scaling Factors ◽  
Noise Source Identification ◽  
Level Analysis

In this study, noise-source identification of a high-speed train was conducted using a microphone array system. The actual sound pressure level analysis of the noise source was performed using scaling factors between the real sound pressure and the beam-power output based on the assumption that the integrated area of the main beam-power lobe is equal to half that of the actual sound pressure of the noise source. Then, the scaling factors for the 144-channel microphone array were derived from analysis of the array response function, and a verification experiment was conducted using a known noise source, an air horn, located on a high-speed train moving at 240 km/h. After the verification test, noise-source identification of the high-speed train was conducted. Based on the resulting noise map of the high-speed train moving at 390 km/h, the main noise sources were determined to be the inter-coach spacing, wheels, and pantograph. The noise generated by the pantograph was then investigated in more detail. It was concluded that the pan head of the pantograph was the main noise source at a frequency of 1000 Hz.

Development of a low cost microphone array system for vehicle noise source identification

2007 ◽  
Vol 122 (5) ◽  
pp. 2965
Author(s):  
Yuri Adson Ribeiro Silva ◽  
Willoium Fonseca ◽  
Samir N. Y. Gerges ◽  
Joel Mobley
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Low Cost ◽  
Vehicle Noise ◽  
Noise Source Identification
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