• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.10-15
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• 1998

Application of the Filtered-X LMS adaptive filter to active noise cancellation requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceler. In this paper, we derive an adaptive cancellation algorithm and analyze is 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 show 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 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2050-2058
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• 2001

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for 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 output and error signal of the adaptive controller. The results of 7he 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 components : Phase estimation error and estimated gain. 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.285-288
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• 2008

In ubiquitous computing environment the biological signal ECG (Electrocardiogram signal) is usually recorded with noise components. Adaptive interference (or noise) canceller do adaptive filtering of the noise reference input to maximally match and subtract out noise or interference from the primary (signal plus noise) input thereby adaptively eliminate unwanted interference from the ECG signal. Measured Stress ECG (or exercise ECG signal) signal have three major noisy component like baseline wander noise, motion artifact noise and EMG (Electro-mayo-cardiogram) noise. These noises are not only distorted signal but also root of incorrect diagnosis while ECG data are analyzed. Motion artifact and EMG noises behave like wide band spectrum signals, and they considerably do overlapping with the ECG spectrum. Here the multidimensional adaptive method used for filtering which is more effective to improve signal to noise ratio.

• The Journal of the Acoustical Society of Korea
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• v.18 no.4
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• pp.38-46
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• 1999

In this paper, two methods were proposed to improve the performance of adaptive feedback cancellation in hearing aids. One is “Orthogonality principle acoustic feedback cancellation method(Orthogonality principle method)” to track optimal solution with monitoring the instantaneous gradient, the other is a method using the CLMS algorithm(CLMS method). In many simulation conditions, adaptive feedback cancellation method proposed in this paper was much better than Greenberg method by Sum-method LMS algorithm which is known the most excellent method by now in case of system mismatch, SNR and segmental SMR. Also. Orthogonality principle method is as good as CLMS method in terms of adaptive feedback cancellation in many simulation conditions.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.377-382
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• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.443-448
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• 2000

In this paper, we propose a novel adaptive feedforward controller for periodic disturbance and noise cancellation, with a frequency tracking capability. It can be added to an existing feedback control system without altering the original closed-loop characteristics, which is based on adaptive algorithm. We introduce novel algorithm "Constrained AFC(adaptive feedforward controller) algorithm" that increase the convergence region regardless of the delay in the closed loop system. In the algorithms, coefficients of the controller are adapted using the residuals of constrained structure which are defined in such a way that the coefficients become time invariant. The proposed algorithm not only estimate the magnitude and phase of the tonal disturbance and noise but also track the frequency of the tone, which changes in quasi-static manner. The frequency tracking algorithm uses the instantaneous frequency approach based on Hilbert transform. A number of computer simulations have been carried out in order to demonstrate the effectiveness of proposed method under various conditions.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.904-906
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• 1995

Based on the detection of the size of datas of multipath channel characterization, we propose a new algorithm. called the impulse size based adaptive median filter(ISMF), for ghost cancellation system. The ISMF consists of two levels. The first one is the impulse noise size detection level and the second one is the adaptive median filtering level to remove the impulse noise detected. Test results confirm that the proposed ISMF removes impulse noise due to multipath channel characterization while preserving signal as well as ghosts so that the LMS algorithm performs effectively.

• Progress in Superconductivity
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• v.4 no.1
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• pp.53-58
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• 2002

Noise from electronic instrumentation is invariably present in biomedical signals, although the art of instrumentation design is such that this noise source may be negligible. And sometimes signals of interest are contaminated or degraded by signals of similar type from another source. Biomedical signals are omni-presently contaminated by these background noises that span nearly all frequency bandwidths. In the magneto-cardiogram (MCG), several digital filters have been designed for the elimination of the power-line interference, broadband white noise, surrounding magnetic noise, and baseline wondering. In addition to the introduced FIR filter, notch, adaptive filter using the least mean square (LMS) algorithm, and recurrent neural network (RNN) filter, a new filtering method for effective noise canceling in MCG signals is proposed in this paper, which is realized by the wavelet packets. The experimental results show that the proposed filter using wavelet packet performs efficiently with respect to noise rejection. To verify this, two characteristics were analyzed and compared with LMS adaptive filter, SNR of filtered signal and attractor pattern using the nonlinear dynamics.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.178-178
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• 2002

In this paper, we propose an adaptive wavelet method for car interior noise cancellation. For this purpose, we use a node dependent threshold which minimizes the Bayesian risk. We propose a noise estimation method based on spectral entropy using histogram of intensity and a candidate best basis instead of Donoho's best bases. And we modify the hard threshold function. Experimental results show that the proposed algorithm is more efficient, especially to heavy noisy signal than conventional one.