• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.132-137
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• 2004

In this paper, the adaptive noise canceler using neural network with backpropagation is designed. The adaptive noise canceler using the least mean square algorithm has the large correlativity of the reference signal. The performance of the adaptive noise canceler shows the limitation when the information signal is relatively small to the noise. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceler is compared with that of the system which is used the least mean square algorithm.

• Speech Sciences
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• v.4 no.2
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• pp.55-68
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• 1998

In this paper, a novel method of noise reduction of speech based on a complex adaptive noise canceler and method of optimal feature extraction are proposed. This complex adaptive noise canceler needs simply the noise detection, and LMS algorithm used to calculate the adaptive filter coefficient. The method of optimal feature extraction requires the variance of noise. The experimental results have shown that the proposed method effectively reduced noise in noisy speech. Optimal feature extraction has shown similar characteristics in noise-free speech.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.179-182
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• 2002

In this paper, we propose a wavelet based adaptive algorithm which improves the convergence speed and reduces computational complexity using the fast running FIR filtering efficiently We compared the performance of the proposed algorithm with time and frequence domain adaptive algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result, the proposed algorithm is suitable for adaptive signal processing area using speech or acoustic field.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1051-1056
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• 2005

Least mean square(LMS) algorithm is one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation. But the convergence speed of time domain adaptive algorithm is slow when the spread width of eigen values is wide. Moreover we have to choose the step size well for convergency in this paper, we use adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm with variable step size, which Is linear to absolute value of error signal. We applied this algorithm to adaptive noise canceler. Simulation results are presented to compare the performance of the proposed algorithm with the usual algorithms.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.45-48
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• 2005

In this paper, we propose a new fast running FIR filter structure that improves the convergence speed of adaptive signal processing and reduces the computational complexity. The proposed filter is applied to wavelet based adaptive algorithm. Actually we compared the performance of the proposed algorithm with other algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result, We know the proposed algorithm is prefer than the existent algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.2
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• pp.68-73
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• 2002

Many paper about the adaptive algorithm based to LS(Least Square) to improve convergence speed are already presented. In this paper, we propose a wavelet based adaptive algorithm which improves the convergence speed and reduces computational complexity, and adapt two kinds of adaptive noise cancelers using the characteristic of speech signal. We compared the performance of the nosed algorithm with time and frequency domain adaptive algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result the proposed algorithm is suitable for adaptive signal processing area using speech or acoustic signal.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.30-36
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• 2002

This paper describes a nonlinear adaptive noise canceler(ANC) using neural networks(NN) based on filter to make up for the drawback of the conventional ANC with the linear adaptive filter. The proposed ANC was tested its noise rejection performance using broadband time-varying noise signal and compared with the ANC of TDL linear filter. Experimental results show that in cases of nonlinear correlations between the noise of primary input and reference input, the neural network based ANC outperforms the linear ANC with respect to mean square error It is also verified that the recurrent NN adaptive filter is superior to the feedforward NN filter. Thus, we identify that the NN adaptive filter is more effective than the linear adaptive filter for rejection of broadband time-varying noise in the ANC.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.250-255
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• 2005

In this paper, we propose a new fast running FIR filter structure that improves the convergence speed of adaptive signal processing and reduces the computational complexity. The proposed filter is applied to wavelet based adaptive algorithm. Actually we compared the performance of the proposed algorithm with other algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result, the frequency domain algorithm is prefer than the existent time domain. we analyzed the Wavelet algorithm, short-length fast running FIR algorithm, fast-short-length fast running FIR algorithm and proposed algorithm.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.191-194
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• 2000

Frequency domain adaptive filter is effective to communication fields of many computational requirements. In this paper we propose a new variable step size algorithms which improves the convergence speed and reduces computational complexity for frequency domain adaptive filter. we compared MSE of the proposed algorithms with one of normalized FLMS using computer simulation of adaptive noise canceler based on synthesis speech.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2459-2462
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• 2003

In this paper, we Propose a wavelet based adaptive algorithm which improves the convergence speed and reduces computational complexity using the fast running FIR filtering efficiently. We compared the performance of the proposed algorithm with time and frequence domain adaptive algorithm using computer simulation of adaptive noise canceler based on synthesis speech.