• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1003-1008
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• 2001

In beamforming method, source positions are predicted by MUSIC (Multiple Signal Classification) power method and composite sound fields can then be decomposed into each partial field by beamforming, detenninistically without restriction of the distance between reference microphones and sources. However, reference microphone array shape is important in both MUSIC and beamforming method. Thus the present paper describes the effect of the reference microphone array shape.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.838-839
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• 2013

In this paper, optimum beamforming method using spherical microphone array is presented. Beamforming method has been recognized as an important study in localizing sound sources or visualizing acoustic fields in three-dimensional space. Its geometrical arrangement of sensors in space enables to process array signal to analyze the fields of interest by steering array response in three-dimensional.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.164-169
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• 2005

Microphone array techniques are being used widely in wind tunnel measurements for identification of the distributed aerodynamic noise sources on the model being tested. Depending on the frequencies and sound levels, conventional beamforming algorithm has limitation in separating two adjacent sources. Several modifications to the classical beamforming have been developed to enhance way resolution and reduce sidelobe levels. In this Paper the robust adaptive beamforming and the CLEAN algorithm are used to compare to the result of conventional beamforming method. It is found that the CLEAN algorithm is capable of pin-pointing locations of multiple sources nearby, while these sources are unidentifiable with robust adaptive or conventional beamforming techniques.

• Phonetics and Speech Sciences
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• v.4 no.4
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• pp.87-92
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• 2012

Speech enhancement aims to improve speech quality by removing background noise from noisy speech. Independent vector analysis is a type of frequency-domain independent component analysis method that is known to be free from the frequency bin permutation problem in the process of blind source separation from multi-channel inputs. This paper proposed a new method of microphone array based speech enhancement that combines independent vector analysis and beamforming techniques. Independent vector analysis is used to separate speech and noise components from multi-channel noisy speech, and delay-sum beamforming is used to determine the enhanced speech among the separated signals. To verify the effectiveness of the proposed method, experiments for computer simulated multi-channel noisy speech with various signal-to-noise ratios were carried out, and both PESQ and output signal-to-noise ratio were obtained as objective speech quality measures. Experimental results have shown that the proposed method is superior to the conventional microphone array based noise removal approach like GSC beamforming in the speech enhancement.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.92-96
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• 2007

Microphone array beamforming method has been recognized as an important aeroacoustic research field and become a standard technique in localizing sound sources. This method also used in flight acoustic measurement, and especially, it is very useful when measure sounds inside the wind tunnel. In measuring sound which is inside the wind tunnel by traditional beamforming method, there are some errors caused by airstream. The speed and the propagation path of the sound changes as it travel through the airstream. This makes the error which the position of sound is changed a little bit to the down stream direction. In this paper, validation test has made about the correction equation for this wind effects of previous researches. And beamforming including shear layer correction was performed about a sound source in the anechoic open-jet windtunnel.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.6
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• pp.612-618
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• 2008

Microphone array beamforming method has been recognized as an important aeroacoustic research field and become a standard technique in localizing sound sources. This method also used in flight acoustic measurement, and especially, it is very useful when measure sounds inside the wind tunnel. In measuring sound which is inside the wind tunnel by traditional beamforming method, there are some errors caused by airstream. The speed and the propagation path of the sound changes as it travel through the airstream. This makes the error which the position of sound is changed a little bit to the down stream direction. In this paper, validation test has made about the correction equation for this wind effects of previous researches. And beamforming including shear layer correction was performed about a sound source in the anechoic open-jet wind tunnel.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.611-614
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• 2021

In this paper, we propose a widerange array microphone for lecturer tracked with Frequency Modulated Continuous Waveform (FMCW) radar sensor. Time Difference-of-Arrival (TDoA) is often used as audio tracking, but the tracking accuracy is poor because the frequency of the voice is low and the relative frequency change is large. FMCW radar has a simple structure and is used to detect obstacles for vehicles, and the resolution can be archived to several centimeter. It is shown that the sensor is useful for detecting a speaker in open area such as a lecture, and we propose an wide range 4-element array microphone beamforming system. Through some experiments, the proposed system is able to adequately track the location and showed a 8.6dB improvement over the selection of the best microphone.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.816-820
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• 2011

A method for increasing the difference of side-lobe level in spherical microphone array is presented. In array signal processing, it is known that narrow interval between sensors can increase the difference between main lobe and side-lobe of array response which eventually increase the source recognition capability. Recent commercial array being used, however, have shown certain limitation in using the number of sensors due to its costs and geometrical size of array. To overcome this problem, we have adapted MEMS sensors into spherical microphone array. To check out the improvement, two different types of spherical microphone array were designed. One array is composed with 32 regular instrument microphones and the other one is 85 MEMS sensors. Simulation and experiments were conducted on a sinusoidal noise source with two arrays. The time history data were analyzed with spherical harmonic decomposition and beamforming technique. 85 MEMS sensors array showed the improved side-lobe level suppression by more than 4 dB above the frequency content of 2 kHz compared to 32-sensor array.