• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.264-270
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• 2003

Application of the Filtered-X LMS adaptive filter to active noise control requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceler. In this paper, we derive an adaptive control algorithm and analyze its convergence behavior when the acoustic noise is assumed to consist of multiple sinusoids. The results of the convergence analysis of the Filtered-X LMS algorithm indicate that the effects of parameter estimation inaccuracy on the convergence behavior of the algorithm are characterize by two distinct components : Phase estimation error and estimated magnitude. In particular, the convergence of the Filtered-X LMS algorithm is shown to be strongly affected by the accuracy of the phase response estimate. Simulation results of the algorithm are presented which support the theoretical convergence analysis.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.48-59
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• 2000

Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.869-878
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• 2009

Image restoration techniques such as noise reduction and contrast enhancement have been researched for enhancing a contaminated image by the noise. An image degraded by additive random noise can be enhanced by noise reduction. Sigma filtering is one of the most widely used method to reduce the noise. In this paper, we propose a new sigma filter algorithm based on noise variance estimation which effectively enhances the degraded image by noise. Specifically, the Bartlett test is used to measure the degree of noise with respect to the degree of image feature. Simulation results are also given to show the performance of the proposed algorithm.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.177-184
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• 2020

In this paper, we studied to direction of arrival (DoA) estimation to use DoA and optimum weight algorithms in coherent interference channels. The DoA algorithm have been considerable attention in signal processing with coherent signals and a limited number of snapshots in a noise and an interference environment. This paper is a proposed method for the desired signal estimation using MUSIC algorithm and adaptive beamforming to compare classical subspace techniques. Also, the proposed method is combined the updated weight value with LCMV beamforming algorithm in adaptive antenna array system for direction of arrival estimation of desired signal. The proposed algorithm can be used with combination to MUSIC algorithm, linearly constrained minimum variance beamforming (LCMV) and the weight value method to accurately desired signal estimation. Through simulation, we compare the proposed method with classical direction of in order to desired signals estimation. We show that the propose method has achieved good resolution performance better that classical direction arrival estimation algorithm. The simulation results show the effectiveness of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.87-94
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• 2009

Noise estimation and suppression are a crucial factor of many speech communication and recognition systems. In this paper, proposed algorithm is based on the ratio of variance normalized of noisy power spectrum in time-frequency domain. Our proposed algorithm tracks the threshold and controls the trade-off between residual noise and distortion. This algorithm is evaluated by the ITU-T P.835 signal distortion (SIG) and segment signal to noise ratio (SNR), and is superior to the conventional methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2299-2301
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• 2009

The adaptive IIR filters for active noise control systems are more effective when acoustic feedback exists, but the adaptive IIR filters could be unstable when the filter algorithm is not yet converged. In this paper, auto-secondary path estimation techniques and a stabilizing process for adaptive Multi-Channel Recursive LMS (MCRLMS) filters are developed to improve the stability of multi-channel active noise control systems. Experiments using a TMS320VC33 digital signal processor in a three dimensional enclosure have performed to show the effectiveness of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.183-190
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• 2013

In this paper, we propose a spatially adaptive noise detection and removal algorithm for a single degraded image. Under the assumption that an observed image is Gaussian-distributed, the noise information is estimated by local statistics of degraded image, and the degree of the additive noise is detected by the local statistics of the estimated noise. In addition, we describe a noise removal method taking a modified Gaussian filter which is adaptively determined by filter parameters and window size. The experimental results demonstrate the capability of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.34 no.5
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• pp.377-384
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• 2015

An active noise control (ANC) algorithm for noise canceling headphone is proposed. In this study, the feedback ANC operated with the filtered-x least mean square algorithm (FxLMS) algorithm is used to attenuate the undesired noise. Also an adaptive residual music canceller (RMC) is proposed for enhancing the accuracy of the reference signal of the feedback ANC. Simulation results show that a high quality of music sound can be consistently achieved in a time-varying secondary path situation.

• The Journal of the Acoustical Society of Korea
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• v.35 no.3
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• pp.234-242
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• 2016

In this paper, an on-line noise coherence estimation algorithm for binaural speech enhancement system is proposed. A number of noise Power Spectral Density (PSD) estimation algorithms based on the noise coherence between two microphones have been proposed to improve the speech enhancement performance. In the conventional algorithms, the noise coherence was characterized using a real-valued analytic model. However, unlike the analytic model, the noise coherence between the two microphones is time-varying in real environments. Thus, in this paper, the noise coherence is updated in accordance with the variation of the acoustic environment to track the realistic noise coherence. The noise coherence can be updated only during the absence of speech, and the simulation results demonstrate the superiority of the proposed algorithm over the conventional algorithms based on the analytic model.