• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.201-204
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• 2000

음성신호에 섞인 잡음을 처리하기 위해서 단 일 마이크로폰을 이용한 방법이 많이 연구되고 있는데, 그 중에서 Kalman 필터를 이용한 방법은 먼저 음성신호의 모델을 검출하고 잡음이 섞인 신호에서 표준 Kalman 필터를 이용해서 음성신호 성분만을 검출하게 된다. 본 논문에서는 음성신호에 섞인 유색잡음을 백색화하는 방법을 적용하여 Kalman 필터의 잡음제거 성능을 향상시키는 방법을 제안하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1665-1674
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• 2011

In this paper, we proposed an algorithm for speech enhancement of speeches corrupted by colored noise. When there is no correlation between colored noise and speech signal, the colored noise turns into white noise through whitening transformation. This transformed signal has been applied to the generalized subspace approach for speech enhancement. The speech spectral distortion, produced by the whitening transformation as pre-processing, has been restored by using the inverse whitening transformation as post-processing of the proposed algorithm. The performance of the proposed algorithm for speech enhancement has been confirmed by computer simulation. The colored noises used in this experiment were car noise and multi-talker babble. It is confirmed that the proposed algorithm shows better performance from SNR and SSD viewpoint over the previous approach with the data from the AURORA and TIMIT data base.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1293-1298
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• 2013

This paper proposes a noise reduction algorithm using Wiener filter to remove the noise components from the noisy speech in order to improve the speech signal. The proposed algorithm first removes the noise spectrums of white noise from the noisy signal based on the noise reshaping and reduction method at each frame. And this algorithm enhances the speech signal using Wiener filter based on linear predictive coding analysis. In this experiment, experimental results of the proposed algorithm demonstrate using the speech and noise data by Japanese male speaker. Based on measuring the spectral distortion (SD) measure, experiments confirm that the proposed algorithm is effective for the speech by contaminated white noise. From the experiments, the maximum improvement in the output SD values was 4.94 dB better for white noise compared with former Wiener filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1078-1084
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• 2010

This paper presents a speech enhancement system which restores the amplitude components and phase components by discrete Fourier transform (DFT), using neural network training by back-propagation algorithm. First, a neural network is trained using DFT amplitude components and phase components of noisy speech signal, then the proposed system enhances speech signals that are degraded by white noise using a neural network. Experimental results demonstrate that speech signals degraded by white noise are enhanced by the proposed system using the neural network, whose inputs are DFT amplitude components and phase components. Based on measuring spectral distortion measurement, experiments confirm that the proposed system is effective for white noise.

• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.45-49
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• 2013

Electrocardiogram(ECG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of cadiac disease diagnosis with removing signal white-noise. Sampling signal was made with generating white-noise. The noise were removed using wavelet transforms and thresholding. Removed noise were compared numerical using SNR(signal to noise ratio). The results compared SNR showed that SURE method was 5.931, 4.9301 in 3, 5dB noise, uninversal was 3.6590, 1.9698 in 7, 9dB noise. De-noising by Thresholding removed noise effectively. ECG signal is expected to improve the accuracy of cadiac desease dianosis.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1295-1303
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• 2010

In this paper, an effective method has been proposed for selection of white Gaussian noise(WGN) band for radio background noise measurement system under broad band noise environment. MMN which comes from industrial devices and equipment mostly happens in the shape of broad band noise mostly like impulsive noise and this is the main reason for increasing level in the present radio noise measurements. The existing method based on singular value decomposition has weak point that it cannot give good performance for the broad band signal because it uses signal's white property. The proposed method overcomes such a weakness of singular value decomposition based method by using signal's Gaussian property based method in parallel. Moreover, this proposed method hires a modelling based method which uses parameter estimation algorithm like maximum likelihood estimation(MLE) and gives more accurate result than the method using amplitude probability distribution(APD) graph. Experiment results under the natural environment has done to verify feasibility of the proposed method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.21-22
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• 2016

우울증은 감정을 조절하는 뇌의 기능의 변화가 생겨 부정적인 감정이 나타나는 병이다. 불안장애와 우울증은일반인구중 15%가 평생 동안 한번 이상 앓는 질환이다. 우울증환자는 일반인보다 불안한감정의 원인인 델타파가 많거나 좌측 전두엽의 알파파가 증가하고, 우측 전두엽은 베타파가 증가하는 특징을 가지고 있다. 선행연구에서는 백색잡음을 우울증환자의 증상완화에 사용하였다. 우울증 환자에게는 백색잡음보다는 유색(갈색)잡음이 치료에 더 효과적인 연구를 기반으로 하여 무음 상태, 갈색잡음, 고주파 가중치를 적용한 갈색잡음, 청감특성을 고려한 가중치를 적용한 갈색잡음을 들었을 때 의 뇌파에 대하여 살펴보았다. 그 중 갈색잡음과 청감특성을 고려한 가중치를 적용한 갈색잡음의 경우가 가장효과가 좋았다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.

• Journal of Ocean Engineering and Technology
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• v.5 no.2
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• pp.191-191
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• 1991

• Journal of Korean Ophthalmic Optics Society
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• v.17 no.2
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• pp.203-207
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• 2012

Purpose: Electroretinogram(ERG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of retinal-related diagnosis with removing signal noise. Methods: Sampling signal was made with generating 60 Hz noise and white noise. The noise were removed using wavelet transforms and bandpass filter. De-noising frequency was compared with Fourier transform spectrum. Removed noises were compared numerically using SNR(signal to noise ratio). Results: The result compared Fourier transform spectrum was showed that 60 Hz noise removed completely and most of white noise was removed by wavelet transforms. 60 Hz and the white noise remained using bandpass filters. The result compared SNR showed that wavelet transforms was 22.8638 and bandpass filter was 4.0961. Conclusions: Wavelet transform showed less signal distortion in removing noise. ERG signal is expected to improve the accuracy of retinal-related diagnosis.