• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.454-460
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• 2009

In this paper, we propose a residual echo and noise reduction scheme by using linear predictor for hands-free telephony applications. The proposed scheme whitens residual echo by the linear prediction during the non double-talk. But whitened residual echo signal still has speech characteristics. In this scheme, the whitened residual echo signal is more whitened by using the power of the linear prediction error signal and the linear predicted signal. After whitening process, near-end speech and ambient noise is present during double-talk but white noise will appear during non double-talk situation. By linearly predicting again the combined signal of the near-end speech and the whitened signal, the ambient noise is removed. Through computer simulation, it is shown that the proposed method performs well at the side of AIC (acoustic interference cancellation).

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.35-43
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• 1999

In this paper, a new robust adaptive algorithm and a post-processing method are proposed to improve the performance of AEC without computational burden. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. To reduce the residual echoes, a new post-processing method, which is co-operated with the proposed noise-robust adaptive algorithm, is proposed in this paper. The method is based on the correlation of the desired signal and the estimation error signal. The residual echoes are attenuated as proportional to the correlation normalized with the power of desired signals. The normalized correlation plays a role as Wiener filter for residual echoes. In the double-talk situation, the estimation error signals, that are residual echoes, dominantly include the near-end speaker's speech and the normalized correlation closes to 1. Therefore, the near-end speaker's speech can be transmitted without being attenuated. When the desired signals consists of only the acoustic echoes, the residual echoes are mostly attenuated and canceled by the proposed post-processor. The computation of AEC using the proposed post-processor is comparable to NLMS algorithm.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.721-740
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• 2019

Noise reduction is an important field in image processing and requires a statistical approach. However, it is difficult to assume a specific distribution of noise, and a spatial filter that reflects regional characteristics is a small sample and cannot be accessed in a parametric manner. The first order image differential and the second order image differential show a clear difference according to the noise level included in the image and can be more clearly understood using the canyon edge detector. The Fligner-Killeen test was performed and the bootstrap method was used to statistically check the noise level. The estimated noise level was set between 0 and 1 using the cumulative distribution function of the beta distribution. In this paper, we propose a nonparametric noise reduction algorithm that accounts for the noise level included in the image.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.15-20
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• 2003

For some applications such as teleconference or telecommunication systems using a distant-talking hands-free microphone, the near-end speech signals to be transmitted is disturbed by an ambient noise and by an echo which is due to the coupling between the microphone and the loudspeaker. Furthermore, the environmental noise including channel distortion or additive noise is assumed to affect the original input speech. In the present paper, a new approach using echo canceller and maximum a posteriori(MAP) estimation is introduced to improve the accuracy of hands-free speech recognition. In this approach, it was shown that the proposed system was effective for hands-free speech recognition in ambient noise environment including echo. The experimental results also showed that the combination system between echo canceller and MAP environmental adaptation technique were well adapted to echo and noise environment.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.129-132
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• 2000

본 논문에서는 음향 반향 제거기의 새로운 동시 통화 검출기를 제안하여 근단화자신호에 의한 적응 필터의 계수 오조정을 줄이고자 하였다. 제안한 동시 통화 검출기는 마이크 입력 신호의전력과 추정 오차 신호와 마이크 입력 신호간의 상호 상관의 비를 이용하여 동시 통화를 검출하는 방법이다. 제안한 동시 통화 검출기는 주변 잡음이 없는 상황에서는 다른 동시 통화 검출기와 비교하여 우수한 성능을 보였다. 그리고 전체적인 음향 반향 제거기의 향상을 가져왔다.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.389-396
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• 1997

Several types of electrical artifact seen on electroencephalogram( EEG) records are described. Those are the EOG and the PVC roller pump noise, and so on. An adaptive digital filtering of the electroencephalogram( EEG) is a successful way of suppressing mains interference, but it affects some of the frequency components of the signal, whore artifacts may not be acceptable in some cafes of automatic EEG processing. Thus we studied the method for cancelling these artifacts. This proposed method does not use the reference channel, and is realized by connecting the linear predictor and the fixed FIR filter for the EOG artifact, and by cascading the linear predictor and the noise canceller for the pump artifact. The simulation results illustrate the performances of the proposed method in terms of the capability of interferences suppression. In the results we obtained about 20 dB noise reduction.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.945-951
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• 2007

Noise cancellation is essential for the devices which use speech as an interface. In real environments, speech quality and recognition rates are degraded by the auditive noises coming near the microphone. In this paper, we propose a noise cancellation algorithm using stereo microphones basically. The advantage of the use of multiple microphones is that the direction information of the target source could be applied. The proposed noise canceller is based on the Wiener filter. To estimate the filter, noise and target speech frequency responses should be known and they are estimated by the spectral classification in the frequency domain. The performance of the proposed algorithm is compared with that of the well-known Frost algorithm and the generalized sidelobe canceller (GSC) with an adaptation mode controller (AMC). As performance measures, the perceptual evaluation of speech quality (PESQ), which is the most widely used among various objective speech quality methods, and speech recognition rates are adopted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.33-39
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• 2018

An additive noise reduction algorithm for a mass spectrum analyzer is proposed. From the measured ion signal, we first used an estimated threshold from the mode of the measured signal to eliminate background noises with the white Gaussian characteristics. Also, a signal block corresponding to each mass index is constructed to perform a second order curve fitting and a linear approximation to signal block. In this process, the effective signal block composed of only the ion signal can be reconstructed by removing the impulsive noises and the sample signals which are insufficient to be viewed as normal ion signals. By performing curve fitting on the effective signal block, the noise-free mass spectrum can be obtained. To evaluate the performance of the proposed method, a simulation was performed using the signals acquired from the development equipment. Simulation results show the validity of the threshold setting from the mode and the superiority of the proposed curve fitting and linear approximation based noise canceling algorithm.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.169-172
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• 2000

본 논문에서는 차량 환경에서 핸즈프리 단말기를 위한 잔향반향제거 방법을 제안한다. 제안된 방법은 기존의 음향반향제거와 잡음제거의 결합구조에 근거하며, 음성신호의 스펙트럼 특성을 배경잡음화함으로써 잔여 반향제거 성능을 향상시킨다. 일반적으로 음향반향제거에서 실제 충격응답보다 적은 차수의 적응필터를 이용할 경우 잔여반향의 전력이 증가하며, 잡음제거기법을 적용하여 잔여반향성분을 줄일 수 있다. 음성신호가 입력되는 음향반향제거기의 잔여반향을 효과적으로 제거하기 위해 음성신호의 AR 스펙트럼에 따른 역필터링을 수행함으로써 잡음제거기에 의한 잔여반향제거 성능을 향상시킬 수 있다. 제안된 기법은 현재 상용화되고 있는 이동통신용 음성부호화기에 포함된 잡음제거기법과 결합하여 사용할 경우 매우 적은 부가 계산량만으로 구현할 수 있다.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.24-29
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• 2012

The sounds of exhaust emissions of automobiles are independent sound sources which are nothing to do with voices. We have no information for the sources of voices and exhaust sounds. Accordingly, Independent Component Analysis which is one of the Blind Source Separaton methods was used to segregate two source signals from each mixed signals. Maximum Likelyhood Estimation was applied to the signals came through the stereo microphone to segregate the two source signals toward the maximization of independence. Since there is no clue to find whether it is speech signal or not, the coefficients of the slope was calculated by the autocovariances of the signals in frequcency domain. Noise remover for speech signals was implemented by coupling the two algorithms.