• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.737-742
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• 2007

The method of the reducing duct noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequencies (below 500Hz) and is limited by the space. On the other hand, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases slightly so it may delay the convergence time when the FXLMS algorithm is applied to the active control of duct noise. Thus the Co-FXLMS algorithm was developed to improve the control performance in order to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing duct noise.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.63-75
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• 2010

In this paper, the equalizer techniques that is able to adopt the digital on-channel repeater for 8VSB-based DTV system has been analyzed and we propose an effective equalizer structure which can reduce the error propagation phenomenon by the feedback signal and improve the receiver performance at the same time. In order to confirm the effective cancellation of the feedback signal, the multi-level Correlation LMS scheme is proposed through the analysis of conventional basic LMS based DFE and Correlation LMS algorithm and as compared with the conventional method, we can confirm the reduction of error propagation. When performing the computer simulation, as the Brazil channel model which is very popular for DTV broadcasting system is adopted, the result is drawn by comparing and analysing the equalizer algorithm. We have examine the symbol error rate which is in the range of 15~25dB of operation receipt SNR and MSE(Mean Square Error) in the DTV broadcasting system. As a result of comparing with the existing method, the signal-noise ratio which is necessary for maintain the bit error correction ability that the means of proposal is same is reduced by about 2~5dB, and in the rate of convergence through the MSE, we found the reduction of needed time.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.267-270
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• 2014

This paper presents the comparative performance of an adaptive FIR filter for a Delayed LMS algorithm. The delayed error signal was used to obtain a Delayed LMS algorithm to allow efficient pipelining for achieving a small critical path and area efficient implementation. This paper presents hardware efficient results (device utilization parameters) and power consumed. The FPGA families (Artix-7, Virtex-7, and Kintex-7) for a low voltage perspective are shown. The synthesis results showed that the artix-7 CMOS family achieves the lowest power consumption of 1.118 mW with 83.18 % device utilization. Different Precision strategies, such as the speed optimization and power optimization, were imposed to achieve these results. The algorithm was implemented using MATLAB (2013b) and synthesized on the Leonardo spectrum.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.247-256
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• 2021

In this paper, we propose a packet loss concealment (PLC) algorithm using pitch harmonic motion prediction and adaptive signal amplitude prediction and. The spectral motion prediction method divides the spectral motion of the previous usable frame into predetermined sub-bands to predict and restore the motion of the lost signal. In the proposed algorithm, the speech signal is classified into voiced and unvoiced sounds. In the case of voiced sounds, it is further divided into pitch harmonics using the pitch frequency to predict and restore the pitch harmonic motion of the lost frame, and for the unvoiced sound, the lost frame is restored using the spectral motion prediction method. When the continuous loss of speech frames occurs, a method of adjusting the gain using the least mean square (LMS) predictor is proposed. The performance of the proposed algorithm was evaluated through the objective evaluation method, PESQ (Perceptual Evaluation of Speech Quality) and was showed MOS 0.1 improvement over the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1528-1535
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• 1994

In this paper, a filtered-X LMS algorithm by new error path identification method is proposed for active noise control system. The proposed algorithm identifies accurately the error path transfer function using three microphones and the control of error signal through double loop scheme with on-line. In the computer simulation using the sinusoidal and the practical duct noise, the proposed algorithm reduces noise level about 29.1dB and 10.4dB, respectively. We can observe the improvement of about 0.5dB and 2.5dB in noise level compared with that obtained using the filtered-X LMS algorithm of Eriksson model.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.425-433
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• 2003

Acoustic Echo Canceller (AEC) is a signal processing system for removing unwanted echo signals in teleconference and hands-free communication. Least mean square (LMS) algorithm is one of the adaptive echo cancellation algorithms and it has been most attractive because of its simplicity and robustness. However, the convergence properties of the LMS algorithm degrade with highly correlated input signals such as speech. For this reason, transform-domain adaptive filtering algorithm was introduced to decorrelate the colored input samples by using the orthogonal transform matrix such as DCT, DFT and then LMS adaptive filtering process is applied. In this paper, we propose a MLT domain adaptive echo canceller base on the MLT (Modulated lapped Transform) orthogonal transform matrix. The proposed algorithm achieves high decorrelation efficiency and fast convergence speed via modulated lapped transform of size 2NXN instead of NXN unitary transform such as DCT, DFT, Hadamad and it is applied to the acoustical echo cancellation system. Form the computer simulation with both synthesis and real speech, the proposed MLT domain adaptive echo canceller shows approximately twice faster convergence speed and 20∼30 ㏈ ERLE improvements over the DCT frequency domain acoustic echo cancellation system.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.19-25
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• 2012

An adaptive system is a well-known method for removing noise from noise-corrupted speech. In this paper, we perform a least mean square (LMS) based on wavelet adaptive algorithm. It establishes the faster convergence rate of as compared to time domain because of eigenvalue distribution width. And this paper provides the basic tool required for the FIR algorithm whose algorithm reduces the arithmetic complexity. We consider a new fast short-length running FIR structure in discrete wavelet adaptive algorithm. We compare FIR algorithm and short-length fast running FIR algorithm (SFIR) to the proposed fast short-length running FIR algorithm(FSFIR) for arithmetic complexities.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.1-6
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• 2016

As the convergence speed of time domain adaptive algorithm on the LMS(Least Mean Square) becomes slow when eigen value distribution width is spread, So variable step size algorithm is used widely. But it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and improve convergence speed, uses instantaneous absolute value of average noise signal adapting the exponential function. For the performance of proposed algorithm is tested and simulated to system generator. As the result we show the variable step size adaptive algorithm in proportion to instantaneous absolute value is more stable and efficient than others.

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.153-160
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• 2016

This study proposes an algorithm for removal of CPR artifact in order that automated external defibrillator (AED) can effectively diagnose ECG rhythm during cardiopulmonary resuscitation (CPR). Current AED required to interrupt chest compression for reliable rhythm analysis to avoid the effect of artifacts produced by CPR. However even temporarily interruption of chest compression during CPR adversely affects the probability of restoration of spontaneous circulation (ROSC) and survival after the delivery of the shock. Therefore, we proposed a method for removal of CPR artifacts using least mean square (LMS) filter. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. It was tested on 31 segments of shockable and 300 segments of non-shockable ECG signals recorded from three pigs during CPR. In the result, sensitivity (Se) and specificity (Sp) analysis on the test segments showed values of Se = 3.2%, Sp = 66.0% and Se = 96.8%, Sp = 98.7% in the case of unfiltered and filtered signals during CPR. In conclusion, it was shown that the proposed method can be a useful tool to exactly diagnose the ECG rhythm during the CPR.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.88-94
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• 2013

In this paper, an efficient signal interference control technique to improve the convergence speed of LMS algorithm is introduced. The convergence characteristics of the proposed algorithm,whose coefficients are multiply adapted in a symbol time period by recycling the received data,are analyzed to prove theoretically the improvement of convergence speed. According as thestep-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the average squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.