• Journal of Biomedical Engineering Research
• /
• v.9 no.1
• /
• pp.101-108
• /
• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.2
• /
• pp.111-116
• /
• 2009

In this paper, we propose a phase offset estimation algorithm which is based on turbo coded digital broadcasting system. The phase estimator is an estimator outside turbo code decoder using LMS (Least Mean Square) algorithm to estimate the phase of next state. While the conventional LMS algorithm with a fixed step size is easy implemented, it has weak points that are difficult the channel estimation and tracking in the multipath environment. To resolve this problem, we propose new phase offset estimation method with a variable step size LMS (VS-LMS). Additionally, we propose a scheme which consists of a conventional LMS. The performance is verified by computer simulation according to a fixed phase offset and a increased phase offset, the proposed algorithm improve the bit error rate performance than the conventional algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.27 no.2E
• /
• pp.64-68
• /
• 2008

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square(VEDNSS LMS) algorithm. Adopting VEDNSS LMS results in higher system complexity, but noise is reduced providing fast convergence speed Mathematical analysis demonstrates that tap coefficient misadjustment converges. This is confirmed by computer simulation with the proposed algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.13 no.4
• /
• pp.239-246
• /
• 2003

Application of the filtered-x Least Mean Square(LMS) adaptive filter to active noise control requires to estimate the transfer characteristics between the output and the error signal of the adaptive controller. In this paper, we derive the filtered-x adaptive noise control algorithm and analyze its convergence behavior when the acoustic noise consists of multiple sinusoids. The results of the convergence analysis of the filtered-x LMS algorithm indicate 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. Simulation results are presented to support the theoretical convergence analysis.

• Journal of Korea Society of Industrial Information Systems
• /
• v.4 no.3
• /
• pp.79-88
• /
• 1999

One of the most popular algorithm in adaptive signal processing is the least mean square(LMS) algorithm. The majority of these papers examine the LMS algorithm with a constant step size. The choice of the step size reflects a tradeoff between misadjustment and the speed of adaptation. Subsequent works have discussed the issue of optimization of the step size or methods of varying the step size to improve performance. However there is as yet no detailed analysis of a variable step size algorithm that is capable of giving both the speed of adaptation and convergence. In this paper we propose a new variable step size algorithm where the step size adjustment is controlled by square of the prediction error. The simulation results obtained using the new algorithm about noise canceller system and system identification are described. They are compared to the results obtained for other variable step size algorithm. function.

• Journal of Communications and Networks
• /
• v.9 no.1
• /
• pp.18-27
• /
• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.7 no.2
• /
• pp.39-50
• /
• 1988

It is proposed to use a pair of frequency-domain adaptive digital filters to estimate the magnitude squared coherence (MSC) functions of two signals. Such a method requires less computations than the LMS-MSC algorithm in which the least mean square (LMS) algorithm is applied in the time domain to compute the coefficients of a pair of adaptive digital filters. The frequency-domain adaptive digital filtering algorithms considered in this paper include the constrained frequency domain LMS (CFLMS) and the unconstrained frequency domain LMS (UFLMS) algorithms. The performance of the proposed methods are compared with those of the LMS-MSC algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.8
• /
• pp.54-62
• /
• 2000

Adaptive filtering method is one of signal processing area which is frequently used in the case of statistical characteristic change in time-varing situation. The performance of adaptive filter is usually evaluated with complexity of its structure, convergence speed and misadjustment. The structure of adaptive filter must be simple and its speed of adaptation must be fast for real-time implementation. In this paper, we propose chirp discrete cosine transform (CDCT), which has the characteristics of CZT (chrip z-transform) and DCT (discrete cosine transform), and then CDCTLMS (chirp discrete cosine transform LMS) using the above mentioned algorithm for the improvement of its speed of adaptation. Using loaming curve, we prove that the proposed method is superior to the conventional US (normalized LMS) algorithm and DCTLMS (discrete cosine transform LMS) algorithm. Also, we show the real application for the ultrasonic signal processing.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.1
• /
• pp.37-41
• /
• 2002

In this paper, we made shorter EDT(early decay time) of room reverberation curve using multiple-channel. The speech signal was processed inverse filtering with full-band and sub-band in the basis MINT, and then the multiple-channel adaptive filters were used LMS (Least Mean Square) and NLMS (Normalized Least Mean Square) algorithm. Experimental results, we could get 1/3 of time reduction at 20dB level in the reverberation curve using full-band NLMS when two microphones were used. Also, it is shown that the speech articulation was improved 80% from the test listeners with the speech, which was to shorten EDT by MINT in the subjective assessments using real room impulse response.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.3
• /
• pp.156-162
• /
• 2018

The LMF (Least Mean Fourth) algorithm is well known for its fast convergence and low steady-state error especially in non-Gaussian noise environments. Recently, there has been increasing interest in the LMS (Least Mean Square) algorithms with self-adjusted step size. It is because the self-adjusted step-size LMS algorithms have shown to outperform the conventional fixed step-size LMS in the various situations. In this paper, a self-adjusted step-size LMF algorithm is proposed, which adopts an averaged gradient based step size as a self-adjusted step size. It is expected that the proposed algorithm also outperforms the conventional fixed step-size LMF. The superiority of the proposed algorithm is confirmed by the simulations in the time invariant and time variant channels.