• The Journal of the Acoustical Society of Korea
• /
• v.42 no.4
• /
• pp.364-369
• /
• 2023

Time-delay estimation between two receivers is a technique that has been applied in a variety of fields, from underwater acoustics to room acoustics and robotics. There are two types of time delay estimation techniques: one that estimates the amount of time delay from the correlation between receivers, and the other that parametrically models the time delay between receivers and estimates the parameters by system recognition. The latter has the characteristic that only a small fraction of the system's parameters are directly related to the delay. This characteristic can be exploited to improve the accuracy of the estimation by methods such as Lasso regularization. However, in the case of Lasso regularization, the necessary information is lost. In this paper, we propose a method using Elastic Net that adds Ridge regularization to Lasso regularization to compensate for this. Comparing the proposed method with the conventional Generalized Cross Correlation (GCC) method and the method using Lasso regularization, we show that the estimation variance is very small even for white Gaussian signal sources and colored signal sources.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.4
• /
• pp.279-284
• /
• 2017

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative time delay between two or more received signals for the direct signal must be determined. Although the GCC (Generalized Cross-Correlation) method is the most popular technique, an approach based on CCA (Canonical Correlation Analysis) was also proposed for the TDE (Time Delay Estimation). In this paper, we propose a new adaptive algorithm based on CCA in order to utilized the sparsity in the eigenvector of CCA based time delay estimator. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue with log-sum regularization in order to utilize the sparsity in the eigenvector. We have performed simulations for several SNR(signal to noise ratio)s, showing that the new CCA based algorithm can estimate the time delays more accurately than the conventional CCA and GCC based TDE algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.7A
• /
• pp.520-528
• /
• 2003

This paper proposes a method of implementing synthetic stereo and an acoustic echo cancellation algorithm for multiple participant conference system. Synthetic stereo is generated by HRTF and two loudspeakers. A robust adaptive algorithm for synthetic stereo echo cancellation is proposed to reduce the weight misalignment due to near-end speech signals and ambient noises. The proposed adaptive algorithm is modified version of SMAP algorithm and the coefficients of adaptive filter is updated with cross correlation of input and estimation error signal normalized with sum of the autocorrelation of input signal and the power of the estimation error signal multiplied with projection order. This is more robust to projection order and ambient noise than conventional SMAP. Computer simulation show that the proposed algorithm effectively attenuates synthetic stereo acoustic echo.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.471-473
• /
• 2009

In this paper, we propose a double talk and noise robust acoustic echo canceler for hands-free telephony applications. The proposed system includes a double-talk detection method that detects the double-talk durations, which uses covariance between microphone input signa and estimated microphone input signal. And proposed adaptive algorithm for estimating acoustic echo path, uses normalized auto-covariance matrix of input signal with multiplication of residual error power and projection order of AP(affine projeciton) algorithm. It is confirmed that the proposed algorithm shows better performance from acoustic interference cancellation (AIC) viewpoint in double talk and noisy environments.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.7
• /
• pp.582-589
• /
• 2012

This paper describes the algorithm recognizing car type from the image received from UAV and the recognition results between three types of car images. Using the NCC(Normalized Cross-Correlation) algorithm, geometric information is matched from template images. Template images are obtained from UAV and satellite map and indoor experiment is performed using satellite map. After verification of the possibility, experiment for verification of same car type recognition is performed using small UAV. In the experiment, same type cars are matched with 0.6 point similarity and truck with similar color distribution is not matched with template image of a sedan.

• Composites Research
• /
• v.29 no.1
• /
• pp.1-6
• /
• 2016

Low-speed impact was localized on a stiffened composite structure, using 4 FBG sensors with 100 kHz-sampling rate interrogator and devised localization algorithm. The composite specimen consists of a main spar and several stringers, and the overall size of the specimen's surface is about $0.8{\times}1.2m$. Pre-stored reference data for 247 grid locations and 36 stiffener locations are gathered and used as comparison target for a random impact signal. The proposed algorithm uses the normalized cross-correlation method to compare the similarities of the two signals; the correlation results for each sensor's signal are multiplied by others, enabling mutual compensation. 20 verification points were successfully localized with a maximum error of 43.4 mm and an average error of 17.0 mm. For the same experimental setup, the performance of the proposed method is evaluated by reducing the number of sensors. It is revealed that the mutual compensation between the sensors is most effective in the case of a two sensor combination. For the sensor combination of FBG #1 and #2, the maximum localization error was 42.5 mm, with average error of 17.4 mm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.5
• /
• pp.375-382
• /
• 2016

In this paper, we presents a signatures verification by using the nonlinear quantization histogram of polar coordinate based on multi-dimensional adjacent pixel intensity difference. The multi-dimensional adjacent pixel intensity difference is calculated from an intensity difference between a pair of pixels in a horizontal, vertical, diagonal, and opposite diagonal directions centering around the reference pixel. The polar coordinate is converted from the rectangular coordinate by making a pair of horizontal and vertical difference, and diagonal and opposite diagonal difference, respectively. The nonlinear quantization histogram is also calculated from nonuniformly quantizing the polar coordinate value by using the Lloyd algorithm, which is the recursive method. The polar coordinate histogram of 4-directional intensity difference is applied not only for more considering the corelation between pixels but also for reducing the calculation load by decreasing the number of histogram. The nonlinear quantization is also applied not only to still more reflect an attribute of intensity variations between pixels but also to obtain the low level histogram. The proposed method has been applied to verified 90(3 persons * 30 signatures/person) images of 256*256 pixels based on a matching measures of city-block, Euclidean, ordinal value, and normalized cross-correlation coefficient. The experimental results show that the proposed method has a superior to the linear quantization histogram, and Euclidean distance is also the optimal matching measure.