• 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.22 no.10
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• pp.2339-2347
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• 1997

The performance of the adaptive noise canceller using LMS algorithm is degraded by the gradient noise due to target speech signals. An adaptive noise canceller with speech detector was proposed to reduce this performande degradation. The speech detector utilized the adaptive prediction-error filter adapted by the NLMS algorithm. This paper discusses to enhance the performance of the adaptive noise canceller forthecorlored noise. The affine projection algorithm, which is known as faster than NLMS algorithm for correlated signals, is used to adapt the adaptive filter and the adaptive prediction error filter. When the voice signals are detected by the speech detector, coefficients of adaptive filter are adapted by the sign-error afine projection algorithm which is modified to reduce the miaslignment of adaptive filter coefficients. Otherwirse, they are adapted by affine projection algorithm. To obtain better performance, the proper step size of sign-error affine projection algorithm is discussed. As resutls of computer simulation, it is shown that the performance of the proposed ANC is better than that of conventional one.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.873-878
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• 1994

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 canceller. We analyze the effects of estimation accuracy on the convergence behavior of the canceller when the input noise is modeled as a sinusoid.

• Speech Sciences
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• v.8 no.2
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• pp.73-81
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• 2001

In this paper, we focus on the real time implementation of the single channel adaptive noise canceller(ANC) by using TMS320C30 EVM board. The implemented single channel adaptive noise canceller is based on a reference paper [1] in which it is simulated by using the recursive average magnitude difference function(AMDF) to get a properly delayed input speech on a sample basis as a reference signal and normalized least mean square(NLMS) algorithm. To certify results of the real time implementation, we measured the processing time of the ANC and enhancement ratio according to various signalto-noise ratios(SNRs). Experimental results demonstrate that the processing time of the speech signal of 32ms length with delay estimation of every 10 samples is about 26.3 ms, and almost the same performance as given in [1] is obtained with the implemented system.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.482-489
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• 2009

This paper proposes a CW(Continuous-Wave) bio-radar applying a new adaptive noise canceller based on ALE(Adaptive Line Enhancer) which can remove the Gaussian noise and system noise. Recently the research works on this CW bio-radar which can be used to detect heartbeat and respiration are advanced by the university and research facility. Although the researches describe CW bio-radar not only is vulnerable for the Gaussian noise but also has a disadvantage of decreasing the heart-rate accuracy due to the noise, the researches do not demonstrate the effective method for removing the noise component in a baseband signal. In this paper, a CW bio-radar applying the new adaptive noise canceller based on ALE which can remove the noise component is proposed. This paper compares and analyzes the performance for increasing the heart-rate accuracy according to removing the Gaussian noise and system noise in the baseband signal through the quadrature receiver which can alleviate the demodulation sensitivity to target position.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.345-351
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• 2004

This Paper describes a nonlinear adaptive noise filter using neural network for digital controller system. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental reaults show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary input is divided by unit and each divided part is processed for very short time than all the processed data are unified to whole data.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.640-644
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• 2010

When we talk with hands-free in a car or noisy lobby, the performance of the echo canceller degrade because background noise added to echo caused by the distance from mouth to microphone is relatively long. It gives a reason for necessity of noise-robust and high convergence speed adaptive algorithm. And if acoustic echo canceller operated not perfectly, residual signal going through the echo canceller to far-end speaker remains residual echo, which degrade quality of talk. To solve this problem, post-processing needed to remove residual echo ones more. In this paper, we propose a new acoustic echo canceller, which has noise robust and high convergence speed, linked with linear predictor as a post-processor. By computer simulation, it is confirmed that the proposed algorithm shows better performance from acoustic interference cancellation (AIC) viewpoint.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1219-1228
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• 1991

In the actual case, a model of noise canceller using adaptive filter has both a channel transfer function from noise source to main signal input and to noise canceller input. The previous models of noise canceller have been considered to be only one side channel transfer function. Therefore, it is proposed that a new model has two channel transfer functions and derives an optimal tranfer function of adaptive noise canceller. The adaptive filter is using the joint process lattice filter that has fast adaptive speed. The signal noise radio has been improved by a model of ANC-ALE and it is confirmed with computer simulation. Beside, a dc bias is very effective for noise cancelling, especially to the particular signal.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.125-130
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• 2003

This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1456-1467
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• 1990

The time domain adaptive noise canceller (time domain ANC) with the adaptive compensator and its algorithm, so called compensated least mean squares(CLMS) algorithm, had been introduced to improve the performance of ANC[1]. In this paper the time domain ANC with the adaptive compensator is transformed into the frequency domain ANC with the adaptive ocmpensator. An compensated frequency-domain least mean squares(CFLMS) algorithm that can adapt the proposed frequency domain ANC is presented.