• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.7-14
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• 2000

Recently, the technologies of adaptive noise cancellation(ANC) are developed fast and widely due to the highly sophisticated digital signal processing algorithms and the high-speed communication networks and devices. But, thousand numbers of the adaptive filter taps are required to obtain the satisfying results in the fields of the adaptive noise cancellation and echo cancellation. In the paper, performance comparisons based on the real-time processing between frequency domain adaptive filter(FDAF) and multi-delay frequency domain adaptive filter(MDF) are carried. Those algorithms provide us with the reductions of the computational burdens and the increase of the convergence rate for the lengthy Fill adaptive filters. The time delay due to the long taps of FDAF can be reduced by adopting the MDF algorithms. The conventional ANC and cross talks ANC using FDAF are implemented on the dSP ACE 1103 real-time signal processing board.

• The Journal of the Acoustical Society of Korea
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• v.34 no.2
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• pp.163-170
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• 2015

In this paper, it is shown that the performance of the frequency-domain multi-reference narrowband noise canceller is determined by the narrowband component to the broadband disturbance power ratio in the reference signals. To overcome this problem, a new narrowband ANC is proposed, where the update of the adaptive filter is determined based on SNR of the reference inputs being measured using the magnitude squared coherence (MSC) between the primary and the reference signals. Simulation results show that the proposed ANC has superior performance over the conventional one.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.231-238
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• 2004

This paper presents the high speed noise reduction processing system using the modified discrete fourier transform(MDFT) on the frequency domain. The proposed filter uses the linear prediction coefficients of the adaptive line enhance(ALE) method based on the Sign algorithm The signals with a random noise tracking performance are examined through computer simulations. It is confirmed that the fast adaptive digital filter is realized by the high speed adaptive noise reduction(HANR) algorithm with rapid convergence on the frequency domain(FD).

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.128-137
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• 2012

When a CCD camera acquires images in the low light level environment, not only the image signals but also noise components are amplified by the AGC (auto gain control) circuit. Since the noise level in the images acquired in the dark is very high, it is difficult to remove noise with existing denoising algorithms that are targeting the images taken in the normal light condition. In this paper, we proposed an adaptive denoising algorithm that can efficiently remove significant noises caused by the low light level. First, the window including a target pixel is transformed to the frequency domain. Then the algorithm compares the characteristics of equally divided four frequency bands. Finally the noises are adaptively removed according to the frequency characteristics. The proposed algorithm successfully improves the quality of low light level images than the existing algorithms do.

• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.167-173
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• 2003

Measuring subtle neuromagnetic signals requires eliminating background noises. Especially, a SQUID magnetometer is very sensitive to the magnetic noise even from a distant source. As typical software methods, we use the synthetic gradiometer of the adaptive filtering to reduce the noises. In this article, we present noise reduction effects in our 37-channel SQUID magnetometer system by applying each method including the frequency-domain adaptive filtering and discuss a selective application of the methods to the detection of clinical magnetoencephalogram signals.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.87-96
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• 2014

In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.2
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• pp.68-73
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• 2002

Many paper about the adaptive algorithm based to LS(Least Square) to improve convergence speed are already presented. In this paper, we propose a wavelet based adaptive algorithm which improves the convergence speed and reduces computational complexity, and adapt two kinds of adaptive noise cancelers using the characteristic of speech signal. We compared the performance of the nosed algorithm with time and frequency domain adaptive algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result the proposed algorithm is suitable for adaptive signal processing area using speech or acoustic signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.911-916
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• 2010

This paper proposes a new algorithm to cancel the acoustic feedback and noise signals in digital hearing aids. The proposed algorithm combines the feedback canceller to remove acoustic feedback signals and the noise canceller to reduce background noises. The feedback canceller is implemented by normal adaptive FIR filter, and the noise canceller is implemented by using the Wiener solution in frequency domain. This noise canceller has the transfer function presented by the power spectral density of signals. To verify the performances of the proposed algorithm, the simulations were carried out for the system. As the results of simulations, it was proved that we can advance 10.85dB output SNR on the average for the forward path gain of 0dB, and 11.04dB output SNR on the average for the forward path gain of 6dB, in the case of using the proposed algorithm.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.89-95
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• 2006

The performance of blind source separation(BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. A post-processing method proposed in this paper was designed to remove the residual component precisely. The proposed method used modified NLMS(normalized least mean square) filter in frequency domain, to estimate cross-talk path that causes residual cross-talk components. Residual cross-talk components in one channel is correspond to direct components in another channel. Therefore, we can estimate cross-talk path using another channel input signals from adaptive filter. Step size is normalized by input signal power in conventional NLMS filter, but it is normalized by sum of input signal power and error signal power in modified NLMS filter. By using this method, we can prevent misadjustment of filter weights. The estimated residual cross-talk components are subtracted by non-stationary spectral subtraction. The computer simulation results using speech signals show that the proposed method improves the noise reduction ratio(NRR) by approximately 3dB on conventional FDICA.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.23-28
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• 2013

In this paper, background noise reduction method using dual microphone is proposed in mobile environment. Each Signal, reference and primary, would be replaced by microphone input signals, which were measured by reference and primary microphones, and then, noise reduction was performed using FDAF. After then, residual and background noise would be estimated and reduced by MMSE-LSA. For consistent noise reduction performance, result of VAD that could be caculated by PLD between two microphones was used.