• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.568-574
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• 2002

The main issue of noise reduction of image is how to preserve edge and reduce noise. Usually, The Wiener falter is used for this purpose. But the conventional Wiener filter cannot remove noise well in both edge and smooth region due to the single size estimation window. In addition, it ignores the correlation between pixels. In this paper, we propose a new noise reduction algorithm, in which adaptive estimation window is used according to property of smooth region and edge region. In order to make edge more clear, directional Gaussian mask and directional estimation window combines to the Wiener filter according to direction of edge. From the simulation results, it can be seen that the proposed algorithm showed improves performance in both PSNR arid subjective evaluation

• Journal of Navigation and Port Research
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• v.31 no.9
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• pp.763-769
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• 2007

A narrow loop noise bandwidth method is desirable to reduce the error of raw measurements due to the thermal noise. However, it degrades the performance of GPS initial synchronization such as mean acquisition time. And it restricts the loop noise bandwidth to a fixed value determined by the lower bound of the allowable range of carrier-to-noise power ratio, so that it is difficult to optimally track GPS signal. In order to make up for the weak points of the fixed-type narrow loop noise bandwidth method and simultaneously minimize the error of code and carrier measurements, this paper proposes a stepwise-type adaptive bandwidth algorithm for DGPS reference receivers. In this paper, it is shown that the proposed adaptive bandwidth algorithm can provide more accurate measurements than those of the fixed-type narrow loop noise bandwidth method, in view of analyzing the simulation results between two signal tracking algorithms. This paper also carries out sensitivity analysis of the proposed adaptive bandwidth algorithm due to the estimation uncertainty of carrier-to-noise power ratio. Finally the analysis results are verified by the experiment using GPS simulator.

• Speech Sciences
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• v.8 no.2
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• pp.73-81
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• 2001

In this paper, we focus on the real time implementation of the single channel adaptive noise canceller(ANC) by using TMS320C30 EVM board. The implemented single channel adaptive noise canceller is based on a reference paper [1] in which it is simulated by using the recursive average magnitude difference function(AMDF) to get a properly delayed input speech on a sample basis as a reference signal and normalized least mean square(NLMS) algorithm. To certify results of the real time implementation, we measured the processing time of the ANC and enhancement ratio according to various signalto-noise ratios(SNRs). Experimental results demonstrate that the processing time of the speech signal of 32ms length with delay estimation of every 10 samples is about 26.3 ms, and almost the same performance as given in [1] is obtained with the implemented system.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.437-442
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• 2015

Voice activity detection is very important process that voice activity separated form noisy speech signal for speech enhance. Over the past few years, many studies have been made on voice activity detection, but it has poor performance in low signal to noise ratio environment or fickle noise such as car noise. In this paper, it proposed new voice activity detection algorithm using ensemble variance based on wavelet band entropy and soft thresholding method. We conduct a survey in a lot of signal to noise ratio environment of car noise to evaluate performance of the proposed algorithm and confirmed performance of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2050-2058
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• 2001

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of 7he convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.66-73
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• 1996

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise control(ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1506-1516
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• 1995

In this paper, we propose the filtered-x least mean fourth (FXLMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the FXLMF adaptive filter to active noise control requires to estimate the transfer characteristics of the acoustic path between the output and the error signal of the adaptive controller. The results of the convergence analysis of the FXLMF algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that the convergence behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06d
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• pp.80-83
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• 1998

In this paper, we propose a new filtered-x least mean fouth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior or a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the ouput and error signal of the adaptive canceller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct component . Phase estimation error and estimated again. In particular , the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2928-2947
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• 2015

Adaptive Gaussian filter (AGF) is a recently developed switching filter to remove salt and pepper noise. AGF first directly identifies pixels of gray levels 0 and 255 as noise pixels, and then only restored noise pixels using a Gaussian filter with adaptive variance based on the estimated noise density. AGF usually achieves better denoising effect in comparison with other filters. However, AGF still fails to obtain good denoising effect on images with noise-free pixels of gray levels 0 and 255, due to its severe false alarm in its noise detection stage. To alleviate this issue, a modified version of AGF is proposed in this paper. Specifically, the proposed filter first performs noise detection via an image block based noise density estimation and sequential noise density guided rectification on the noise detection result of AGF. Then, a modified Gaussian filter with adaptive variance and window size is used to restore the detected noise pixels. The proposed filter has been extensively evaluated on two representative grayscale images and the Berkeley image dataset BSDS300 with 300 images. Experimental results showed that the proposed filter achieved better denoising effect over the state-of-the-art filters, especially on images with noise-free pixels of gray levels 0 and 255.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.499-505
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• 2018

Time delay estimation between two acoustic sensors is widely used in room acoustics and sonar for target position estimation, tracking and synchronization. A cross-correlation based method is representative for the time delay estimation. However, this method does not have enough consideration for the noise added to the receiving acoustic sensors. This paper proposes a new time delay estimation method considering the added noise on the receiver acoustic sensors. From comparing with the existing GCC (Generalized Cross Correlation) method, and adaptive eigen decomposition method, we show that the proposed method outperforms other methods for a colored signal source in the white Gaussian noise condition.