• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.695-698
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• 2009

본 논문에서는 음원으로부터 나온 음과 동일한 음을 들을 수 있는 시스템을 구축하는 것을 목적으로 하여 이 두 개의 음으로부터 전달되어온 음장의 상태를 구하여 이 역 필터를 구성하는 방법을 연구한다. 본 논문에서는 최소 2승 평균법(Least Mean Square, LMS)을 사용하여 FIR 필터(Finite Impulse Response)의 계수를 계산하여 이를 갱신함으로써 역 필터법을 구축하는 방법을 사용한다. 또한 이 방법과는 별도로 LMS법의 부분을 신경회로망에 대처하는 알고리즘을 제안하였다. 시뮬레이션 실험으로부터 상당히 간단한 파형에 비선형인 왜곡이 있는 것을 본 논문에서 제안한 신경회로망에 의한 학습 가능한 것을 확인하였다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.33-41
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• 1994

A single channel adaptive noise canceling (ANC) technique with a recursive time delay estimator (RTDE) is presented for removing effects of additive noise on the speech signal. While the conventional method makes a reference signal for the adaptive filter using the pitch estimated on a frame basis from the input speech, the proposed method makes the reference signal using the delay estimated recursively on a sample-by-sample basis. As the RTDEs, the recursion formulae of autocorrelation function (ACF) and average magnitude difference function (AMDF) are derived. The normalized least mean square (NLMS) and recursive least square (RLS) algorithms are applied for adaptation of filter coefficients. Experimental results with noisy speech demonstrate that the proposed method improves the perceived speech quality as well as the signal-to-noise ratio and cepstral distance when compared with the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.1-8
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• 2007

LMS(Least Mean Square) algorithm using steepest descent way in adaptive signal processing requires simple equation and is used widely because of the less complexity. But eigenvalues change by width of input signals in time domain, so the rate of convergence becomes low. In this paper, we propose a new fast running FIR filter structure that improves the convergence speed of adaptive signal processing and the same performance as the existing fast wavelet transform algorithm with less computational complexity. The proposed filter structure is applied to wavelet based adaptive algorithm. Simulation results show a better performance than the existing one.

• Progress in Superconductivity
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• v.5 no.1
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• pp.26-30
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• 2003

Magneto-cardiogram(MCG) signals may be highly distorted by the environmental noise, such as power-line interference, broadband white noise, surrounding magnetic noise, and baseline wondering. Several kinds of digital filters and noise cancellation methods have been designed and realized by many researchers, but these methods gave some problems that the original signal may be distorted by digital filter due to the wideband characteristics of background noise. To eliminate noise effectively without distortion of MCG signals, we performed multi-level frequency decomposition using wavelet packets and local adaptive noise cancellation in each local frequency range. In addition to the proposed wavelet filter to eliminate these various non-stationary noise elements, the local adaptive filter using the least mean square(LMS) algorithm and the soft threshold do-noising method are introduced in this paper. The signal to noise ratio(SNR) and the reconstruction square error(RSE) are calculated to evaluate the performance of the proposed method and compared with the results of the conventional wavelet filter and adaptive filter. The experimental results show that the proposed local adaptive filtering method is better than the conventional methods.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.455-463
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• 2003

Adaptive noise cancellation techniques are ideally suitable for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. Grain noises have an un-correlation property, while flaw echoes are correlated. Thus, adaptive filtering algorithms use the correlation properties of signals to enhance the signal-to-noise ratio (SNR) of the output signal. In this paper, a multi-stage adaptive noise cancellation (MANC) method using adaptive least mean square error (LMSE) filter for enhancing flaw detection in ultrasonic signals is proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.830-841
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• 2000

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at iteration a upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RL algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the (B+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.29-35
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• 1984

LMS algorithm is used widely in adaptive filtering because of its simplicity. In this paper it is shown that the one dimensional LMS adaptive filter can be extended in the two dimensional adaptive filter and the methods for improving the convergence rate and the several problems inherent in the two dimensional adaptive filter are discussed.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.517-524
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• 1995

This paper is a study on the design of the cascade adaptive filter (CAF) for baseline wandering elimination in order to enhance the performance of the detection of ST segments in ECG. The CAF using Least Mean Square (LMS) algorithm consists of two filters. The primary adaptive filter which has the cutoff frequency of 0.3Hz eliminates the baseline wandering in raw ECG The secondary adaptive filter removes the remnant baseline wandering which is not eliminated by the primary adaptive filter. The performance of the CAF was compared with the standard filter, the recursive filter, and the adaptive impulse correlated filter (AICF). As a result, the CAF showed a lower signal distortion than the standard filter and the AICF. Also, the CAF showed a better perf'ormance in noise elimination than the standard filter and the recursive filter. In conclusion, considering the characteristics of the noise elimination and the signal distortion, the CAF shows a better performance in the removal of the baseline wandering than the other three Otters and suggests the high performance in the detection of ST segment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1274-1285
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• 1991

This paper presents a statistical convergence analysis of the normalized least mean square(NLMS)algorithm that employs a single-pole lowpass filter, In this algorithm the lowpass filter is used to adjust its output towards the estimated value of the input signal power recursively. The estimated input signal power so obtained at each time is then used to normalize the convergence parameter. Under the assumption that the primary and reference inputs to the adaptive filter are zero mean wide sense stationary, and Gaussian random processes, and further making use of the independence assumption. we derive expressions that characterize the mean and maen squared behavior of the filter coefficients as well as the mean squared estimation error. Conditions for the mean and mean squared convergence are explored. Comparisons are also made between the performance of the NLMS algorithm and that of the popular least mean square(LMS) algorithm Finally, experimental results that show very good agreement between the analytical and emprincal results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.57-60
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• 2003

We have proposed the adaptive noise reduction algorithm using the MDFT. The algorithm proposed use the linear prediction coefficients of the AR method based on Sign algorithm that is the modified LMS instead of the least mean square(LMS). The signals with a random noise tracking performance are examined through computer simulations and confirmed that the high speed adaptive noise reduction processing system is realized with rapid convergence.