• 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.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.871-886
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• 2011

This paper proposes a powerful SVM-ASM filter, the adaptive switching median(ASM) filter based on support vector machines(SVMs), to effectively reduce impulse noise in corrupted images while preserving image details and features. The proposed SVM-ASM filter is composed of two stages: SVM impulse detection and ASM filtering. SVM impulse detection determines whether the pixels are corrupted by noise or not according to an optimal discrimination function. ASM filtering implements the image filtering with a variable window size to effectively remove the noisy pixels determined by the SVM impulse detection. Experimental results show that the SVM-ASM filter performs significantly better than many other existing filters for denoising impulse noise even in highly corrupted images with regard to noise suppression and detail preservation. The SVM-ASM filter is also extremely robust with respect to various test images and various percentages of image noise.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1210-1216
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• 2009

In this paper we analyze the convergence behavior of the recursive least rank (RLR) L-filter. The RLR L-filter is an order statistics filter, filter coefficients of which are the weights according to the order of magnitude of inputs. And RLR L-filter is a non-linear adaptive filter, that uses RLR algorithm for coefficient updating. The RLR algorithm is a non-linear adaptive algorithm based on rank estimates in Robust statistics. The mean and mean-squared convergence behavior of the RLR L-filter is examined with variable step-sizes. The RLR L-filter adapts the median filter type to the heavy-tailed distribution function of impulse noise, and adapts the average filter type to Gaussian noises.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.353-360
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• 2017

The single sensor based acoustic reflection control system separates the incident and reflected signals from the single sensor output, and reduces the reflected signal by generating an out-of-phase signal from the incident signal component. In this paper, we propose an optimal filter design method for a single sensor based reflection control system. In the proposed method, it is shown that the optimum control filter design is possible by using the measured impulse responses of the reflection and control paths. The reflection control algorithm based on the proposed optimal filter achieves better performance than the conventional adaptive filter-based algorithm and effectively controls the reflection without the initial convergence time. We performed computer simulations using the signals obtained in a 1-dimensional acoustic duct environment, and from the simulation results, it was confirmed that the proposed optimal filter has robust performance even in noisy environment.