• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.04a
• /
• pp.950-952
• /
• 2012

음원 위치추정 시스템은 일반적으로 여러 개의 마이크에서 수집된 음원의 시간 간격을 이용해 음원의 위치를 추정하는 방식을 적용한다. 본 논문에서는 감시카메라에 적합한 4개의 마이크로폰을 이용한 음원 위치추정 시스템에서 마이크로폰에 수신된 음원의 도착순서를 이용해 음원의 위치를 추정하는 알고리즘을 제안하였다. 제안한 알고리즘을 시뮬레이션 프로그램을 통해 검증한 결과, 음원 추정각도의 오차는 $2^{\circ}{\sim}11.25^{\circ}$로 확인되었으며, 이는 실제각도의 오차범위인 $0^{\circ}{\sim}22.5^{\circ}$ 내에 해당하기 때문에 추정각도의 오차가 최대로 발생하더라도 음원이 발생한 위치를 파악 할 수 있음을 의미한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.11a
• /
• pp.111-114
• /
• 2014

본 논문에서는 다양한 스테레오 환경에서도 정확한 음원 위치 추정이 가능한 방법을 제안한다. 기존의 음원 위치 추정 방법은 방향성을 가지고 있는 주성분 신호와 방향성이 없는 주변 성분으로 구성된 스테레오 환경에서만 음원의 위치 추정이 가능했다. 그러나 현재 제공되고 있는 스테레오 신호는 방향성을 가지는 다수의 음원으로 구성되어있고, 기존의 음원 위치 추정 방법으로는 정확한 음원 위치 추정이 어렵다. 이와 같은 문제 때문에 다수의 음원을 분리한 뒤, 음원의 위치를 추정하는 방법이 제안되었다. 그러나 음원의 분리 과정에서 생기는 분리 오차가 커서 음원 위치 추정이 정확하지 않다. 이에 본 논문에서는 정확한 음원 위치 추정을 위하여 음원 분리와 음원 위치 추정이 통합된 새로운 알고리즘을 제안한다. 제안한 알고리즘은 음원 위치를 기존의 방법보다 정확하게 추정하는 것을 확인할 수 있었다.

• Proceedings of the Acoustical Society of Korea Conference
• /
• spring
• /
• pp.210-213
• /
• 1999

수동소나체계에서 음원의 위치와 관련된 매개변수를 산출하기 위해 정합장처리(Matched Field Processing)가 이용된다 본 연구에서는 수직선배열센서를 이용한 정합장처리에서 음원 위치추정에 영향을 미치는 다양한 요인 중, 수직음속분포 오정합(mismatch)에 의한 영향을 MV 프로세서 (Minimum Variance Processor)를 이용하여 모의실험함으로써 그 결과를 분석하였다. 천해 모의환경에서 동일한 기울기로 증감하는 수직음속분포 오정합은 음원 위치추정에서 거리성분의 오차를 가지며, 상이한 기울기를 갖는 수직음속분포 오정합은 거리와 수심 성분의 오차가 유발됨을 확인할 수 있었다. 심해 모의 환경에서 수직음속분포 오정합은 거리와 수심 성분의 오차가 유발되고, 거리추정의 전반적인 경향은 천해의 동일한 기울기를 가지는 경우와 유사함을 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.8
• /
• pp.415-425
• /
• 2007

This paper proposes a method of underwater source localization using the wideband interference patterns matching. By matching two interference patterns in the spectrogram, it is estimated a ratio of the range from source to sensor5, and then this ratio is applied to the Apollonius circle. The Apollonius circle is defined as the locus of all points whose distances from two fixed points are in a constant value so that it is possible to represent the locus of potential source location. The Apollonius circle alone, however still keeps the ambiguity against the correct source location. Therefore another equation is necessary to estimate the unique locus of the source location. By estimating time differences of signal arrivals between source and sensors, the hyperbola equation is used to get the cross point of the two equations, where the point being assumed to be the source position. Simulations are performed to get performances of the proposed algorithm. Also, comparisons with real sea experiment data are made to prove applicability of the algorithm in real environment. The results show that the proposed algorithm successfully estimates the source position within an error bound of 10%.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.1
• /
• pp.47-56
• /
• 2020

The sound source localization technique has various application fields in the era of internet-of-things, for which the probe size becomes critical. The localization methods using the acoustic intensity vector has an advantage of downsizing the layout of the array owing to a small finite-difference error for the short distance between adjacent microphones. In this paper, the acoustic intensity vector and the Time Difference of Arrival (TDoA) method are compared in the viewpoint of the localization error in the far-field. The comparison is made according to the change of spacing between adjacent microphones of the three-dimensional microphone array arranged in a tetrahedral shape. An additional test is conducted in the reverberant field by varying the reverberation time to verify the effectiveness of the methods applied to the actual environments. For estimating the TDoA, the Generalized Cross Correlation-Phase transform (GCC-PHAT) algorithm is adopted in the computation. It is found that the mean localization error of the acoustic intensimetry is 2.9° and that of the GCC-PHAT is 7.3° for T₆₀ = 0.4 s, while the error increases as 9.9°, 13.0° for T₆₀ = 1.0 s, respectively. The data supports that a compact array employing the acoustic intensimetry can localize of the sound source in the actual environment with the moderate reflection conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.2
• /
• pp.24-29
• /
• 2009

Sound source localization system is used in a robot, a video conference and CCTV(Closed-circuit television) systems. In this Sound source localization systems are applied to human and they can receive a number of sound data frames during speaking. In this paper, we propose methods which is reducing angle estimation error by selecting sound data frame which can more precisely compute the angles from inputted sound data frame. After selected data converted to angle, the error of sound source localization recognition system can be reduced by applying to medium filter. By the experiment using proposed system it is shown that the average error of angle estimation in sound source recognition system can be reduced up to 31 %.

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

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.2
• /
• pp.114-119
• /
• 2009

Sound source localization systems in service robot applications estimate the direction of a human voice. Time delay information obtained from a few separate microphones is widely used for the estimation of the sound direction. Correlation is computed in order to calculate the time delay between two signals. Inverse cosine is used when the position of the maximum correlation value is converted to an angle. Because of nonlinear characteristic of inverse cosine, the accuracy of the computed angle is varied depending on the position of the specific sound source. In this paper, we propose an efficient sound source localization system using angle division. By the proposed approach, the region from $0^{\circ}$ to $180^{\circ}$ is divided into three regions and we consider only one of the three regions. Thus considerable amount of computation time is saved. Also, the accuracy of the computed angle is improved since the selected region corresponds to the linear part of the inverse cosine function. By simulations, it is shown that the error of the proposed algorithm is only 31% of that of the conventional a roach.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.4
• /
• pp.318-327
• /
• 2009

In this paper, we propose a 3-D geometric localization method for near-field broadband source in shallow water environments. According to the waveguide invariant theory, slope of the interference pattern which is seen in a sensor spectrogram directly proportional to a range of the source. The relative ratio of the range between source and sensors was estimated by matching of two interference patterns in spectrogram. Then this ratio is applied to the Apollonius's circle which shows the locus of a source whose range ratio from two sensors is constant. Two Apollonius's circles from three sensors make the intersection point that means the horizontal range and the azimuth angle of the source. And this intersection point is constant with source depth. Therefore the source depth can be estimated using 3-D hyperboloid equation whose range difference from two sensors is constant. To evaluate a performance of the proposed localization algorithm, simulation is performed using acoustic propagation program and analysis of localization error is demonstrated. From simulation results, error estimate for range and depth is described within 50 m and 15 m respectively.

• The Journal of the Acoustical Society of Korea
• /
• v.35 no.5
• /
• pp.358-367
• /
• 2016

SSL (Sound Source Localization) has been applied to several applications such as man-machine interface, video conference system, smart car and so on. But in the process of sound source localization, angle estimation error is occurred mainly due to the non-linear characteristics of the sine inverse function. So an approach was proposed to decrease the effect of this non-linear characteristics, which divides the microphone's covering space into narrow regions. In this paper, we proposed an optimal space dividing way according to the pattern of microphone array. In addition, sound source's 2-dimensional position is estimated in order to evaluate the performance of this dividing method. In the experiment, GCC-PHAT (Generalized Cross Correlation PHAse Transform) method that is known to be robust with noisy environments is adopted and triangular pattern of 3 microphones and rectangular pattern of 4 microphones are tested with 100 speech data respectively. The experimental results show that triangular pattern can't estimate the correct position due to the lower space area resolution, but performance of rectangular pattern is dramatically improved with correct estimation rate of 67 %.