• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.26-34
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• 2000

This paper presents a new adaptive impulse noise cancelling technique based on the adaptive nonlinear suppressing function. The proposed "adaptive threshold algorithm (ATA)" is controlled by the normalized power prior input data term, and this adaptive threshold makes the cancelling system highly robust against additive impulse noise. For the performance evaluation, we have tested the proposed algorithm with the observed signals simulated in various impulsive noise environments and real EMG signals. As a result the proposed algorithm shows superior performance of 51.7% to the available techniques in the points of SNR and MSE.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.423-426
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• 2003

Adaptive algorithm used in Acoustic Echo Canceller (AEC) needs fast convergence algorithm when reference signal is colored speech signal. Set-Membership Affine Projection (SMAP) algorithm is derived from the constraint, which is the minimum value adaptive filter coefficient error. In this paper, we test the characteristic about noise of the SMAP algorithm and proposed modified version of SMAP algorithm fur using at AEC. As the projection order increase, the convergence characteristic of the SMAP algorithm is improved where no noise space. But if the noise uncorrelated with input signal exists, the AEC shows bad performance. In this paper, we propose normalized version of adaptive constants using estimated error signal for robust to noise and show the good performance through AEC simulation.

• ETRI Journal
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• v.34 no.1
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• pp.130-133
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• 2012

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.164-169
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• 2005

Microphone array techniques are being used widely in wind tunnel measurements for identification of the distributed aerodynamic noise sources on the model being tested. Depending on the frequencies and sound levels, conventional beamforming algorithm has limitation in separating two adjacent sources. Several modifications to the classical beamforming have been developed to enhance way resolution and reduce sidelobe levels. In this Paper the robust adaptive beamforming and the CLEAN algorithm are used to compare to the result of conventional beamforming method. It is found that the CLEAN algorithm is capable of pin-pointing locations of multiple sources nearby, while these sources are unidentifiable with robust adaptive or conventional beamforming techniques.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1183-1186
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• 1998

Adaptive nois canceller can extract the noiseremoved spech in noisy speech signal by adapting the filter-coefficients to the background noise environment. A kind of LMS algorithm is one of the most popular adaptive algorithm for noise cancellation due to low complexity, good numerical property and the merit of easy implementation. However there is the matter of increasing misadjustment at voiced speech signal. Therefore the demanded speech signal may be extracted. In this paper, we propose a fast and noise robust wavelet packet adaptive noise canceller with NLMS-SUM method LMS combined algorithm. That is, we decompose the frequency of noisy speech signal at the base of the proposed analysis tree structure. NLMS algorithm in low frequency band can efficiently dliminate the effect of the low frequency noise and SUM method LMS algorithm at each high frequency band can remove the high frequency nosie. The proposed wavelet packet adaptive noise canceller is enhanced the more in SNR and according to Itakura-Satio(IS) distance, it is closer to the clean speech signal than any other previous adaptive noise canceller.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.640-644
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• 2010

When we talk with hands-free in a car or noisy lobby, the performance of the echo canceller degrade because background noise added to echo caused by the distance from mouth to microphone is relatively long. It gives a reason for necessity of noise-robust and high convergence speed adaptive algorithm. And if acoustic echo canceller operated not perfectly, residual signal going through the echo canceller to far-end speaker remains residual echo, which degrade quality of talk. To solve this problem, post-processing needed to remove residual echo ones more. In this paper, we propose a new acoustic echo canceller, which has noise robust and high convergence speed, linked with linear predictor as a post-processor. By computer simulation, it is confirmed that the proposed algorithm shows better performance from acoustic interference cancellation (AIC) viewpoint.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.179-182
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• 2002

The development of noise robust speech processing systems is becoming increasingly important as speech technology is currently widely applied in real world applications. Recently, to resolve such a noise problem, adaptive noise canceller(ANC) is frequently used, which is based upon adaptive filters. The adaptive recursive filters perform better than adaptive non-recursive filters due to the added poles, but the stability may be severely threatened. But these problems of adaptive recursive filters was solved by ACHARF algorithm. This paper presents a method which combines speaker verification system with ANC(Adaptive Noise Canceller) using the ACHARF algorithm. In the front-end stage, ANC is adopted to suppress the additive noise imposed on the speech signal. The results show that the performance of speaker verification system becomes better than before.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.322-325
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• 1994

Mareel and Bitmead proved the presence of chaotic signal in random noise by applying dead beat control theory to adaptive mechanism. In this paper robust adaptive theory is proposed. With the property of chaotic signal that has order and law, the proposed theory can enhance the control Performance by applying the recursive algorithm that uses dynamic relation which have small correlation. The performance of proposed algorithm is demonstrated with the computer simulation of position control of electric motor. In this simulation, the adaptive low is adopted to control electric motor and the Presence of chaotic signal in feedback signal is proved by using several method such as time series, fourier spectrum phase portrait method.

• Journal of Korea Multimedia Society
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• v.2 no.1
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• pp.99-108
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• 1999

In this paper, a new robust adaptive algorithm is proposed to improve the performance of AEC without computational burden. The proposed adaptive algorithm is based on NLMS algorithm, and its step-size is varied with the reference input signal power and the desired signal power. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. The convergence speed is comparable to NLMS algorithm at AEC application because the echo signals are attenuated about 10∼20 dBSPL. The characteristics of this algorithm is also analyzed and compared with conventional ones in this paper.