• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2753-2755
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• 2005

최근 들어 공장에서만 사용되는 로봇을 탈피하여, 사람과 함께 생활하면서 서비스를 제공할 수 있는 인간 친화적인 로봇이 전 세계적으로 개발되고 있다. 인간과 자연스런 상호작용을 위하여, 화자를 바라보고, 깨끗한 음성신호를 얻는 과정에서 음원의 위치 추적은 필연적이다. 본 연구는 마이크로폰에 도달되는 동일 신호의 도착시간지연(Time Delay of Arrival)정보를 이용하여, 음원 위치를 구하는 기존의 수학적인 방법론[1]을 소개하고 검토한다. 모의실험을 통하여, 방법론[1]이 허상음원을 생성하는 음원 위치 측정임을 보인다.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.124-132
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• 2023

When the building becomes high, the number of households increases and they are adjacent to the elevator. So, frequency of use of elevators will increase. Elevator noise is bound to increase in the future. However, there are currently no legal standards for elevator noise or measurement and evaluation methods that can clearly measure elevator noise in Korea. Although some methods for measuring elevator noise are presented in KS F ISO 16032, this standard is not a standard established for elevator noise. It is a standard that integrates the overall measurement method of building equipment and equipment, and the position of the microphone is selected by the experimenter during measurement. Elevator noise is characterized by a low sound pressure level as the noise in the mid-low frequency band is important. However, even today, complaints from residents about elevator noise are increasing. In this study, the position of the microphone that can most sensitively pick up the elevator noise when measuring the elevator noise was studied. According to the distance from the wall and the height from the floor, a total of 9 microphone positions were measured and analyzed. As a result of the experiment, it was confirmed that the elevator noise has a very high influence in the 63 Hz band. The measured value at the center point was identified as a factor that lowered the overall elevator noise level value.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.72-77
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• 2024

Recently, the necessity for sound source localization has grown significantly across various industrial sectors. Among the sound source localization methods, sound intensimetry has the advantage of having high accuracy even with a small microphone array. However, the increase in localization error at high Helmholtz numbers have been pointed out as a limitation of this method. The study proposes a method to compensate for the bias error of the measured sound intensity vector according to the Helmholtz numbers by applying deep learning. The method makes it possible to estimate the accurate direction of arrival of the source by applying a dense layer-based deep learning model that derives compensated sound intensity vectors when inputting the sound intensity vectors measured by a tetrahedral microphone array for the Helmholtz numbers. The model is verified based on simulation data for all sound source directions with 0.1 < kd < 3.0. One can find that the deep learning-based approach expands the measurement frequency range when implementing the sound intensimetry-based sound source localization method, also one can make it applicable to various microphone array sizes.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.3-10
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• 2000

This paper proposes acoustic holographic measuring system to estimate an absolute position of sound source. Using the measured signals, the estimation of the position is calculated by the Cross-spectrum algorithm of the beamformed signal and a linear arrayed microphone's signals. As the results of comparing the reference microphone method with beamforming method through the measurement of sound field, the beamforming acoustic holographic method is progressed above 20 percent than that of a reference microphone method in the resolution, and the utility of the proposed system could be confirmed.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1795-1796
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• 2008

과학기술의 발전과 함께 인간형 서비스 로봇에 대한 관심이 고조되고 있다. 서비스 로봇의 핵심 중의 하나는 인간과의 상호작용이라 할 수 있다. 자연스러운 상호작용을 위하여, 화자를 바라보고, 깨끗한 음성신호를 얻는 과정에서 음원의 위치 측정은 필연적이다. 본 논문은 마이크로폰에 도달되는 동일 신호의 도착시간지연(Time Delay of Arrival: TDOA) 특성 행렬을 정의하고, 이를 이용하여 공간상의 화자 위치 측정 방법론을 제안하였다. 휴머노이드 로봇의 머리에 마이크로폰 배열을 구성하였고, 실제 시스템을 통한 실험을 통하여 방향 검지 및 높이 구분을 실행하였다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.04a
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• pp.201-205
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• 1994

본 실험에서는 한개의 마이크로폰만을 이용하여 소음기내에 삽입된 재료의 흡음율을 임피던스관에서 전달함수기법으로 측정하고 측정값의 정확성을 확인하기 위하여 정재파법으로 측정한 결과와 비교하여 보았다. 또한 이 측정법을 단순팽창형 소음기에 적용하여 소음기의 투과손실을 측정한 후, 이론과 비교하여 측정의 정확성을 검토하였고 소음기내에서의 흡음재의 위치에 따른 투과손실의 변화에 대해서도 이론과 비교 검토하였다.

• Journal of the Korean Society for Railway
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• v.15 no.1
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• pp.23-28
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• 2012

In this paper, noise of Korean high-speed trains (KTX) running at different speed from 150 to 300km/h was measured by a microphone array system. From the measurement, relation between maximum sound pressure levels and train moving speeds of KTX was drawn and a regression coefficient from the relation was also derived. Moreover, increases of SPL with speeds of KTX were analyzed in the frequency domain. From the analysis, sound characteristics of passing-by noise of KTX were provided. Then, dominant noise source areas were obtained from the measurements and propagation patterns of KTX in vertical direction were also investigated. Finally, noise sources of KTX were identified from inspection of noise maps.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.975-985
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• 2018

The drones have been developed for military purposes and are now used in many fields such as logistics, communications, agriculture, disaster, defense and media. As the range of use of drones increases, cases of abuse of drones are increasing. It is necessary to develop anti-drone technology to detect the position of unwanted drones using the physical phenomena that occur when the drones fly. In this paper, we estimate the DOA(direction of arrival) of the drone by using the acoustic signal generated when the drone is flying. In addition, the dynamics model of the drones was applied to the unscented kalman filter to improve the microphone array detection performance and reduce the error of the position estimation. Through simulation, the drone detection performance was predicted and verified through experiments.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.178-183
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• 2024

This study introduces an enhanced sound source localization technique, bolstered by a data-driven deep learning approach, to improve the precision and accuracy of direction of arrival estimation. Focused on refining Time Difference Of Arrival (TDOA) based sound source localization, the research hinges on accurately estimating TDOA from cross-correlation functions. Accurately estimating the TDOA still remains a limitation in this research field because the measured value from actual microphones are mixed with a lot of noise. Additionally, the digitization process of acoustic signals introduces quantization errors, associated with the sampling frequency of the measurement system, that limit the precision of TDOA estimation. A deep learning-based approach is designed to overcome these limitations in TDOA accuracy and precision. To validate the method, we conduct comprehensive evaluations using both two and three-microphone array configurations. Moreover, the feasibility and real-world applicability of the suggested method are further substantiated through experiments conducted in an anechoic chamber.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.253-256
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• 2004

일반적으로 빔형성 방법은 다수의 독립된 소음원들을 찾아 내기 위한 매우 효과적인 방법으로 각 어레이 마이크로폰들간의 간격에 따른 위상의 차이를 이용한다. 그러나 이러한 위상차는 파장의 길이와 밀접한 관계를 가지므로 어레이의 형상 및 마이크로폰들의 위치는 주파수에 의해 제한 받으며 빔조정 벡터 또한 주파수의 함수로 여러 주파수가 섞여 있는 경우 한계가 있을 수 밖에 없다. 본 논문에서는 크기가 임의적인 음원들에 대하여 주파수와 무관한 빔조정 벡터를 구성함으로써 위상에 대한 제한 없이 소음원을 찾을 수 있는 방법을 제안하였다.