• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.406-413
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• 2024

The proposed method uses Generalized Cross Correlation - Phase Transform (GCC-PHAT) method with nonlinear preprocessing and a frequency domain low-pass filter. In this paper, by reinterpreting the calculation process of GCC-PHAT as DFT, we derive that there is an effective frequency band used for time delay estimation in GCC-PHAT, and by using only the effective band using a low-pass filter, the noise component is reduced and it improvesthe time delay performance in impulsive noise environments. By comparing the proposed method with the traditional GCC-PHAT in an impulsive noise environment, we show that the GCC-PHAT becomes more robust to the impulsive noise.

• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.271-275
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• 2018

There are several methods for estimating the time delay between incoming signals to two sensors. Among them, the GCC-PHAT (Generalized Cross Correlation-Phase Transform) method, which estimates the relative delay from the signal whitening and the cross-correlation between the different signal inputs to the two sensors, is a traditionally well known method for achieving stable performance. In this paper, we have identified a part of GCC-PHAT that can improve the periodicity. Also, we apply the auto-correlation method that is widely used as a method to improve the periodicity. Comparing the proposed method with the GCC-PHAT method, we show that the proposed method improves the mean square error performance by 5 dB ~ 15 dB at the SNR above 0 dB for white Gaussian signal source and also show that the method improves the mean square error performance up to 15 dB at the SNR above 2 dB for the color signal source.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.270-274
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• 2007

In this paper, we present a Sound Source Localization (SSL) based GCC (Generalized Cross Correlation)-PHAT (Phase Transform) and new measurement method of angle with robot auditory system for a network-based intelligent service robot. The main goal of this paper is to analysis performance of TDOA and GCC-PHAT sound source localization method and new angle measurement method is compared. We use GCC-PHAT for measuring time delays between several microphones. And sound source location is calculated by using time delays and new measurement method of angle. The robot platform used in this work is wever-R2, which is a network-based intelligent service robot developed at Intelligent Robot Research Division in ETRI.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.345-352
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• 2017

We propose a methods which is reducing direction estimation error of sound source in the reverberant and noisy environments. The proposed algorithm divides speech signal into voice and unvoice using VAD. We estimate the direction of source when current frame is voiced. TDOA (Time-Difference of Arrival) between microphone array using the GCC-PHAT (Generalized Cross Correlation with Phase Transform) method will be estimated in that frame. Then, we compare the peak value of cross-correlation of two signals applied to estimated time-delay with other time-delay in time-table in order to improve the accuracy of source location. If the angle of current frame is far different from before and after frame in successive voiced frame, the angle of current frame is replaced with mean value of the estimated angle in before and after frames.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.267-275
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• 2017

Time of difference of arrival (TDOA) method using passive sonar sensor array has normally been used to estimate the location of a concealed moving target in underwater environment. Particle filter has been introduced for effective target estimation for non-Gaussian and nonlinear systems. In this paper, we propose a GPU-based acceleration of target position estimation using particle filter and propose efficient embedded system and software architecture. For the TDOA measurement from the passive sonar sensor, we use the generalized cross correlation phase transform (GCC-PHAT) method to obtain the correlation coefficient of the signal using FFT and we try to accelerate the calculation of GCC-PHAT based TDOA measurements using FFT with GPU CUDA. We also propose parallelization method of the target position estimation algorithm using the GPU CUDA to update the state of each particle for the target position estimation using the measured values. The target estimation algorithm was verified using Matlab and implemented using GPU CUDA. Then, we realized the proposed signal processing acceleration system using NVIDIA Jetson TX1 as the target board to analyze in terms of the execution time. The execution time of the algorithm is reduced by 55% to the CPU standalone-operation on the target board. Experiment results show that the proposed architecture is a feasible solution in terms of high-performance and area-efficient architecture.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.258-263
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• 2010

Sound localization algorithm usually adapts three step process: sampling sound signals, estimating time difference of arrival between microphones, estimate location of sound source. To apply this process in indoor environment, sound localization algorithm must be strong enough against reverberant and noisy condition. Additionally, calculation efficiency must be considered in implementing real-time sound localization system. To implement real-time robust sound localization system we adapt four low cost condenser microphones which reduce the cost and total calculation load. And to get TDOA(Time Differences of Arrival) of microphones we adapt GCC-PHAT(Generalized Cross Correlation-Phase Transform) which is robust algorithm to the reverberant and noise environment. The position of sound source was calculated by using iterative least square algorithm which produce highly accurate position data.

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

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

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.460-465
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• 2021

In active sonar, time delay estimation is used to find the distance between the target and the sonar. Among the time delay estimation methods for active sonar, estimation in the frequency domain is widely used. When estimating in the frequency domain, the time delay can be thought of as a frequency estimator, so it can be used relatively easily. However, this method is prone to rapid increase in error due to noise. In this paper, we propose a new method which applies weighted dictionary and sparsity in order to reduce this error increase and we extend it to two receivers to propose an algorithm for estimating the time delay between two receivers. And the case of applying the proposed method and the case of not applying the proposed method including the conventional frequency domain algorithm and Generalized Cross Correlation-Phase transform (GCC-PHAT) in a white noise environment were compared with one another. And we show that the newly proposed method has a performance gain of about 15 dB to about 60 dB compared to other algorithms.

• Convergence Security Journal
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• v.22 no.2
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.