A three-dimensional sound intensity measurement system for sound source identification and sound power determination by ln models

2005 ◽  
Vol 118 (6) ◽  
pp. 3691-3705 ◽  
Author(s):  
Shiho Nagata ◽  
Kenji Furihata ◽  
Tomohiro Wada ◽  
David K. Asano ◽  
Takesaburo Yanagisawa
Keyword(s):  
Measurement System ◽  
Sound Source ◽  
Source Identification ◽  
Three Dimensional ◽  
Sound Intensity ◽  
Intensity Measurement ◽  
Sound Power ◽  
Sound Source Identification

scholarly journals Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 279
Author(s):  
Shiyi Chai ◽  
Xiaoqin Liu ◽  
Xing Wu ◽  
Yanjiao Xiong
Keyword(s):  
Power Spectrum ◽  
Sound Source ◽  
Three Dimensional ◽  
Sound Intensity ◽  
Point Sources ◽  
Mechanical Equipment ◽  
Sound Power ◽  
Test Bed ◽  
Multiple Sources ◽  
Noise Sources

The identification and separation of sources are the prerequisite of industrial noise control. Industrial machinery usually contains multiple noise sources sharing same-frequency components. There are usually multiple noise sources in mechanical equipment, and there are few effective methods available to separate the spectrum intensity of each sound source. This study tries to solve the problem by the radiation relationship between three-dimensional sound intensity vectors and the power of the sources. When the positions of the probe and the sound source are determined, the sound power of the sound source at each frequency can be solved by the particle swarm optimization algorithm. The solution results at each frequency are combined to obtain the sound power spectrum of each sound source. The proposed method is first verified by a simulation on two point sources. The experiment is carried out on a fault simulation test bed in an ordinary laboratory; we used three three-dimensional sound intensity probes to form a line array and conducted spectrum separation of the nine main noise sources. The sound intensity on the main frequency band of each sound source was close to the result of the near-field measurement of the one-dimensional sound intensity probe. The proposed spectral separation method of the sound power of multiple sound sources provides a new method for accurate noise identification in industrial environments.

Uncertainty of sound power measurements of a reference sound source using the AHRI Standard 230 sound intensity method

2021 ◽  
Vol 263 (6) ◽  
pp. 641-647
Author(s):  
Curtis Eichelberger ◽  
Paul Bauch
Keyword(s):  
Training Program ◽  
Sound Source ◽  
Measurement Method ◽  
Sound Intensity ◽  
Intensity Measurement ◽  
Sound Power ◽  
Sound Sources ◽  
Scanning Method ◽  
Intensity Measurements ◽  
Power Measurements

The uncertainty of determining the sound power of HVAC equipment using the AHRI Standard 230 sound intensity measurement method is presented. Measurements of six different reference sound sources (RSS) at four different laboratories, by nineteen different individuals with four different instrumentation systems are presented. From 2004 through 2020, these measurements were performed as part of a training program at Johnson Controls HVAC test laboratories to qualify technicians and engineers on the use of sound intensity instrumentation. The results illustrate the reproducibility of sound intensity measurements using the scanning method of AHRI Standard 230.

scholarly journals Three-Dimensional Sound Field Reconstruction and Sound Power Estimation by Stereo Vision and Beamforming Technology

2020 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Yetian Cai ◽  
Xiaoqin Liu ◽  
Yanjiao Xiong ◽  
Xing Wu
Keyword(s):  
Stereo Vision ◽  
Sound Source ◽  
Power Level ◽  
Three Dimensional ◽  
Sound Field ◽  
Sound Intensity ◽  
Depth Information ◽  
Sound Power ◽  
Field Reconstruction ◽  
Sound Field Reconstruction

The size of the sound field reconstruction area has an important influence on the beamforming sound source localization method and determines the speed of reconstruction. To reduce the sound field reconstruction area, stereo vision technology is introduced to continuously obtain the three-dimensional surface of the target and reconstruct the sound field on it. The fusion method can quickly locate the three-dimensional position of the sound source, and the computational complexity of this method is mathematically analyzed. The sound power level can be estimated dynamically by the sound intensity scaling method based on beamforming and the depth information of the sound source. Experimental results in a hemi-anechoic chamber show that this method can quickly identify the three-dimensional position of the moving source. When the depth of the moving sound source changes, the estimated sound power is more stable than the sound pressure on the microphone.

scholarly journals An investigation into high-speed train interior noise with operational transfer path analysis method

2021 ◽  
Author(s):  
Muxiao Li ◽  
Ziwei Zhu ◽  
Tiesong Deng ◽  
Xiaozhen Sheng
Keyword(s):  
Sound Source ◽  
Reference Point ◽  
Source Identification ◽  
High Speed ◽  
Interior Noise ◽  
Target Point ◽  
High Speed Train ◽  
Transfer Path ◽  
Sound Source Identification ◽  
Transfer Path Analysis

AbstractPassengers' demands for riding comfort have been getting higher and higher as the high-speed railway develops. Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis (OTPA) method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method, which has high test efficiency and can be carried out during the working state of the targeted machine. The OTPA model is established from the aspects of "path reference point-target point" and "sound source reference point-target point". As for the mechanism of the noise transmission path, an assumption is made that the direct sound propagation is ignored, and the symmetric sound source and the symmetric path are merged. Using the operational test data and the OTPA method, combined with the results of spherical array sound source identification, the path contribution and sound source contribution of the interior noise are analyzed, respectively, from aspects of the total value and spectrum. The results show that the OTPA conforms to the calculation results of the spherical array sound source identification. At low speed, the contribution of the floor path and the contribution of the bogie sources are dominant. When the speed is greater than 300 km/h, the contribution of the roof path is dominant. Moreover, for the carriage with a pantograph, the lifted pantograph is an obvious source. The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation, and the contribution of air excitation is non-significant. Certain analyses of train parts provide guides for the interior noise control.

Sound Source Identification Using Coherence- and Intensity-Based Methods

2007 ◽  
Vol 56 (6) ◽  
pp. 2478-2485 ◽  
Author(s):  
Giovanni Moschioni ◽  
Bortolino Saggin ◽  
Marco Tarabini
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Sound Source Identification
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