• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.158-163
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• 2009

LMS(Least Mean Square) algorithm requires simple equation and is used widely because of the low complexity. If the convergence speed increase, LMS algorithm has a divergence in case of sharp environment changes. And if a stability increase, the convergence speed becomes slow. This algorithm based on a trade off between fast convergence and system stability. To improve this problem, VSSLMS (Variable Step Size LMS) algorithm was developed. The VSSLMS algorithm improved the convergence speed and performance as adjusting step size using error signal. In this paper, I-VSSLMS algorithm is proposed tor improve the performance of adaptive noise canceller in real-time environments. The proposed algorithm is applied to adaptive noise canceller using TMS320C6713 DSP board and we did simulation by real time. Then we compared performance of each algorithm and demonstrated that proposed algorithm has superior performance.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.580-585
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• 1990

This paper presents a real time FECG signal monitoring system in which an adaptive multichannel noise canceller is implemented using a Texas Instruments TMS32020 digital signal processor. Abdominal ECG signal is applied as the desired output and 3 chest ECG signals as the reference input signals of the adaptive multichannel noise canceller whose coefficients are updated using the LMS algorithms.

• Journal of Biomedical Engineering Research
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• v.13 no.2
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• pp.107-114
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• 1992

An adaptive filter for noise cancellation of ECG Is proposed. An adaptive noise canceller using the least mean squares algorithm Is used to reduce unwanted noise. An adaptive filter for nolse cancella lion minimizes the mean-square error between a primary input and a reference input. A primary input is the noisy ECG, and a reference input is a noise that Is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.4
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• pp.42-49
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• 1997

A conventional adaptive noise canceller (ANC) using LMS algorithm suffers from the misadjustment of adaptive filter weights due to the gradient-estimate noise by input speech signal at steady state. In this paper, an ANC is proposed which uses the combination of VSLMS (variable step size LMS) and SA (sign algorithm) to improve steady state performance and convergence rate. SA algorithm is applied in speech region to prevent the weights from perturbing by output speech of ANC and VSLMS algorithm is applied to improve convergence rate and channel tracking ability in silence region and adaptive transient region. In compute rsimulation, the performance of the proposed VSLMS-SA combination algorithm is much better than LMS algorithm and the algorithm, recently proposed by greenberg, with adaptation step-size parameter determine dby sum method in convergence rate, channel tracking and steady state performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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• pp.281-287
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• 1997

In the conventional inverse modeling method for on-line modeling of the secondary path transfer function, the signal to noise ratio between the arbitrary random signal and the plant noise have to keep at -10 - -20 dB. For these reasons, the modeling can't be exactly implemented by the conventional method alone and the convergence time for modeling becomes too long. In this study, by combining the conventional inverse modeling method with an adaptive line enhancer, or with an adaptive noise canceller, a rigorous transfer functions of secondary path modeling and the control of a primary noise have been implemented simultaneously.

• MALSORI
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• no.62
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• pp.85-96
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• 2007

In this paper, we carried out recognition experiments for noisy speech having various levels of car noise and output of an audio system using the speech interface. The speech interface consists of three parts: pre-processing, acoustic echo canceller, post-processing. First, a high pass filter is employed as a pre-processing part to remove some engine noises. Then, an echo canceller implemented by using an FIR-type filter with an NLMS adaptive algorithm is used to remove the music or speech coming from the audio system in a car. As a last part, the MMSE-STSA based speech enhancement method is applied to the out of the echo canceller to remove the residual noise further. For recognition experiments, we generated test signals by adding music to the car noisy speech from Aurora 2 database. The HTK-based continuous HMM system is constructed for a recognition system. Experimental results show that the proposed speech interface is very promising for robust speech recognition in a noisy car environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.105-110
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• 2009

The techniques of intelligent interference cancellation are used for achieving the improvement of deterioration, which is arisen to the interoperable terminal(GSM and DVB-H). In this paper, we propose a novel system that improve the DVB-H received performance by using the method of an adaptive interference canceller for GSM900 and DVB-H terminal. The interference for the collocated GSM900 and DVB-H receiver is cancelled by using the adaptive canceller with the low-noise ADC(Analog to Digital Converter) in the RF stage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.96-102
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• 2010

This paper proposes a new adaptive algorithm to cancel the acoustic feedback and noise signals in the digital hearing aids. The proposed algorithm improves its convergence performances by canceling the speech signal from the residual signal using two microphones. The feedback canceller firstly cancels the feedback signal among the mic signal, and then it is reduced the noise using the beamforming method. To verify the performances of the proposed algorithm, the simulations were carried out for some cases. As the results of simulations, it was proved that the feedback canceller and the noise canceller advance about 14.43 dB for SFR, 10.19 dB for SNR respectively during speech, in the case of using the new algorithm.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.261-268
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• 2003

Conventional acoustic echo cancellers using ES algorithm have simple structure and fast convergence speed compared with those using NLMS algorithm, but they are very weak to external noise because ES algorithm updates the adaptive filter taps based on average energy reduction rate of room impulse response in specific acoustical condition. To solve this problem, in this paper, a new update algorithm for acoustic echo canceller with stepsize matrix generator is proposed. A set of stepsizes is determined based on residual error energy which is estimated by two moving average operators, and applied to the echo canceller in matrix from, resulting in improved convergence speed. Simulations in various noise condition show that the proposed algorithm improves the robustness of acoustic echo canceller to external noise.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1249-1256
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• 2019

In this paper, we propose a new algorithm to remove acoustic feedback and noise in hearing aids. Instead of using the conventional FIR structure, this algorithm is a deep learning algorithm using neural network adaptive prediction filter to improve the feedback and noise reduction performance. The feedback canceller first removes the feedback signal from the microphone signal and then removes the noise using the Wiener filter technique. Noise elimination is to estimate the speech from the speech signal containing noise using the linear prediction model according to the periodicity of the speech signal. In order to ensure stable convergence of two adaptive systems in a loop, coefficient updates of the feedback canceller and noise canceller are separated and converged using the residual error signal generated after the cancellation. In order to verify the performance of the feedback and noise canceller proposed in this study, a simulation program was written and simulated. Experimental results show that the proposed deep learning algorithm improves the signal to feedback ratio(: SFR) of about 10 dB in the feedback canceller and the signal to noise ratio enhancement(: SNRE) of about 3 dB in the noise canceller than the conventional FIR structure.