• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.200-204
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• 2005

The use of beamforming method and de-Dopplerization technique was applied in studying the rotating sound sources. Acoustic analysis of a moving sound source required that the measured sound signals be do-Dopplerized and restored as of the original emission signals. Two main issues of the signal reconstruction in time domain are addressed herein: First, to remove Doppler effect from the measured data and to restore the original emission data of the moving source. The difference of the time domain beamforming from the frequency domain beamforming was mentioned. Also, the time domain beamforming method is deployed in the test and the comparisons were made to the frequency domain results. The time domain signal reconstruction was numerically simulated prior to the application. To validate the de-Dopplerization Performance, the rotating Point sources were examined and localized by the use of a phased array of microphone. The application of prop-rotor was conducted in a hovering condition. The results of reconstructing time signals of rotating sources and its locations were shown in the power distribution maps. In the prop-rotor measurements, the acoustic source locations were successfully verified in varying positions for different frequencies of interest.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.7 s.112
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• pp.677-682
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• 2006

In the case of aeroacoustic test in windtunnel, measurement accuracy is reduced by not only Doppler effects but also by the microphone self noise due to airflow and high turbulence in the wall boundary layer. Microphone array measurements can be easily utilized for the solutions of these problems. In this paper, geometrical optics approach and diagonal term elimination of cross spectral matrix was introduced to the de-dopplerization and self noise reduction methods for the microphone array measurement. For the validation, beamforming tests for sinusoidal point source were performed in the closed type test section of windtunnel, and their performances of beam width and sidelobe rejection were significantly improved.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.822-825
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• 2005

In the case of aeroacoustic test in windtunnel, measurement accuracy is reduced by not only Doppler effects but also by the microphone self noise due to airflow and high turbulence in the wall boundary layer. Microphone array measurements can be easily utilized for the solutions of these problems. In this paper, geometrical optics approach and diagonal term elimination of cross spectral matrix was introduced to the de-dopplerization and self noise reduction methods for the microphone array measurement. For the validation, beamforming tests for sinusoidal point source were performed in the closed type test section of windtunnel, and their performances of beam width and sidelobe rejection were significantly improved.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.911-915
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• 2004

Recently, several experimental techniques for identifying the noise sources distributed over a moving vehicle are being developed and used in order to design a low noise vehicle. The beamforming method, which uses phase information between several microphones to localize the source position, is proved to be one of the promising techniques applicable even under complicated test environments. In this study a beamforming algorithm is developed and applied to measure the dominant noise sources on a passenger car moving at constant speed. Unlike the acoustic signals from a stationary noise source, the sound generated from a moving source is distorted due to the Doppler effects. The sound pressure are measured with an spiral array system composed of 26 microphones and a pair of photo sensors are used to measure the. vehicle speed. The information about the speed and relative position of the vehicle are used to eliminate the Doppler effects from the measured pressure signal by using a de-Dopplerization algorithm. The noise generated from a moving vehicle can be grouped in many ways, however, tire noise and the noise generated from the engine are distinguishable at the speeds being tested.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.238-243
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• 2008

Several experimental techniques for identifying the noise sources distributed over a moving vehicle have been developed recently and are used to design a low noise vehicle. The beamforming method, which uses phase information between several microphones to localize the source position, is proved to be one of the promising techniques applicable even under complicated test environments. In this study a beamforming algorithm is developed and applied to measure the dominant noise sources on a passenger car passing by. Unlike the acoustic signals from a stationary noise source, the sound generated from a moving source is distorted due to the Doppler effects. The information about the speed and relative position of the vehicle are used to eliminate the Doppler effects from the measured acoustic signal by using a de-Dopplerization algorithm. The noise generated from a moving vehicle can be grouped in many ways, however, tire noise and the noise generated from the engine are distinguishable at the speeds being tested.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.586-603
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• 2004

One of the subtle problems that make noise control difficult for engineers is “the invisibility of noise or sound.” The visual image of noise often helps to determine an appropriate means for noise control. There have been many attempts to fulfill this rather challenging objective. Theoretical or numerical means to visualize the sound field have been attempted and as a result, a great deal of progress has been accomplished, for example in the field of visualization of turbulent noise. However, most of the numerical methods are not quite ready to be applied practically to noise control issues. In the meantime, fast progress has made it possible instrumentally by using multiple microphones and fast signal processing systems, although these systems are not perfect but are useful. The state of the art system is recently available but still has many problematic issues : for example, how we can implement the visualized noise field. The constructed noise or sound picture always consists of bias and random errors, and consequently it is often difficult to determine the origin of the noise and the spatial shape of noise, as highlighted in the title. The first part of this paper introduces a brief history, which is associated with “sound visualization,” from Leonardo da Vinci's famous drawing on vortex street (Fig. 1) to modern acoustic holography and what has been accomplished by a line or surface array. The second part introduces the difficulties and the recent studies. These include de-Dopplerization and do-reverberation methods. The former is essential for visualizing a moving noise source, such as cars or trains. The latter relates to what produces noise in a room or closed space. Another mar issue associated this sound/noise visualization is whether or not Ivecan distinguish mutual dependence of noise in space : for example, we are asked to answer the question, “Can we see two birds singing or one bird with two beaks?"