• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.4 s.95
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• pp.410-417
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• 2005

In this paper, optical noise is reduced by a differential detector with a plastic optical fiber bundle in a wireless optical interconnection. A plastic optical fiber bundle divides the received optical signal equally and connects it to two photodiodes. In this configuration two photodiodes effectively detect the optical signal at one point, and the output voltage variation due to the abrupt change of optical noise distribution in space disappears. The signal to noise ratio in a differential detector with a fiber bundle was improved to be $10\;\cal{dB}$ higher than in a single photodiode with an optical filter.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.735-741
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• 2011

In this paper, we have analyzed the effect of SNR estimation error on the BER performance of MMSE-DFE in high-speed binary CDMA system. Since MMSE equalization algorithm requires the SNR value of input signal, it should be estimated using CAZAC sequence in preamble. However, when AWGN and ISI exist simultaneously, it is impossible to estimate the exact SNR value of input signal and thereby equalizer's performance may be deteriorated. The simulation results can be used as a guideline for selection of SNR estimation algorithm for MMSE-DFE design.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.90-98
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• 1996

An auditory model is proposed for robust speech recognition in noisy environments. The model consists of cochlear bandpass filters and nonlinear stages, and represents frequency and intensity information efficiently even in noisy environments. Frequency information of the signal is obtained by zero-crossing intervals, and intensity information is also incorporated by peak detectors and saturating nonlinearities. Also, the robustness of the zero-crossings in estimating frequency is verified by the developed analytic relationship of the variance of the level-crossing interval perturbations as a function of the crossing level values. The proposed auditory model is computationally efficient and free from many unknown parameters compared with other auditory models. Speaker-independent speech recognition experiments demonstrate the robustness of the proposed method.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.315-318
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• 1998

본 논문에서는 서브밴드 및 전 대역(full band)으로부터 얻은 특징 벡터를 함께 사용하여 잡음 환경에서 음성인식 시스템의 성능을 향상시키는 방법을 제안하였다. 이는 인식시 잡음에 오염된 대역에서 얻은 특징 벡터를 제거하는데 따른 정보 손실을 막기 위해 전 대역으로부터 얻은 특징 벡터를 함께 이용하며 신호 대 잡음비가 높은 대역을 강조하여 각 모델에 대한 확률 값을 계산한다. 전화망에서 수집된 데이터베이스를 이용하여 인식 실험을 수행한 결과 비교적 넓은 주파수 대역에 걸쳐 분포된 잡음의 경우에도 인식 성능을 향상시킬 수 있었다.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2000.10a
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• pp.52-53
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• 2000

실제로 어류는 자연 발생적인 잡음과 인위적 잡음이 혼재하는 환경에서 서식하기 때문에 어류의 청각은 수중에서 발생하는 주위의 잡음에 의해서 영향을 받으며 잡음이 클 경우는 작은 음이 듣기 어렵게 되는 마스킹 현상이 발생하게 된다. 따라서 수중에는 각각의 요인에 의해서 발생하는 매경 잡음들이 항시 혼합되어 있고, 그 음압의 레벨도 변동하기 때문에 수중음을 이용하여 어류를 유집하고 어획하기 위해서는 대상 어종에 의한 청각문턱치뿐만 아니라 배경 잡음에 의한 마스킹 효과를 충분히 조사하는 것이 중요하다.(중략)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.102-105
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• 2000

본 논문에서는 잡음환경에서의 음성신호복원(Speech Enhancement) 시스템 구현을 목적으로 한다 이를 위한 적응필터로서 LMS(Least Mean Square)알고리즘 FIR필터를 제안한다. 또 정밀 필터로서 신경망 필터를 제안한다. 잡음환경에서의 음성신호 복원 시스템은 잡음에 의해 왜곡된 음성신호에서 잡음성분만을 제거함으로써 음성신호를 복원하는 시스템이다. 일반적으로 잡음은 시변특성과, 비선형적인 전달특성을 갖는다. 그러므로 파라미터가 고정된 필터로는 제어하기가 힘들다. 이러한 이유로 본 논문에서는 LMS알고리즘 적응필터를 적용한다. 신경망 필터는 오차 역전파 학습 알고리즘에 의해 오차를 최소화하는 방향으로 필터의 파라미터를 수정한다. 제안한 필터로 잡음환경에서의 음성신호복원 시스템을 구성하고, 실험을 통해 필터의 성능을 확인한다.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.199-202
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• 2002

본 논문에서는 사람의 청각 모델을 기반으로 잡음에 의해 손상된 음성 신호로부터 잡음 신호의 마스킹 특성과 신호에너지의 지각(知覺)을 나타내는 임계대역(critical band)에서의 잡음 에너지에 대한 지각 패턴인 noise excitation pattern을 이용한 잡음 에너지 차감과 잡음 추정 오차에 의한 변형된 음성신호 내의 순음(tonal) 성분과 비순음(non-tonal)성분의 보정을 통해 효과적인 음성 품질의 개선을 위한 연구를 하였다.

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.473-482
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• 2009

Reconstructed 3D data from computer vision includes necessarily a noise or an error. When these data goes through a mesh process, the different 3D mesh data from original shape comes to make by a noise or an error. This paper proposed the method that smooths a noise effectively by noise analysis in reconstructed 3D data. Because the proposed method is smooths a noise using the area ratio of the mesh, the pre-processing of unusable mesh is necessary in 3D mesh data. This study detects a peak noise and Gaussian noise using the ratio of 3D volume and 2D area of mesh and smooths the noise with respect of its characteristics. The experimental results using synthetic and real data demonstrated the efficacy and performance of proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.95-102
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• 2008

This paper presents an appropriate approach for modeling noise in Computed Radiography(CR) images of high strength materials. The approach is specifically designed for types of noise with the statistical and nonlinear properties. CR images Ere degraded even before they are encoded by computer process. Various types of noise often contribute to contaminate radiography image, although they are detected on digitalization. Quantum noise, which is Poisson distributed, is a shot noise, but the photon distribution on Image Plate(IP) of CR system is not always Poisson process. The statistical properties are relative and case-dependant due to its material characteristics. The usual assumption of a distribution of Poisson, binomial and Gaussian statistics are considered. Nonlinear effect is also represented in the process of statistical noise model. It leads to estimate the noise variance in regions from high to low intensity, specifying analytical model. The analysis approach is tested on a database of steel tube step-wedge CR images. The results are available for the comparative parameter studies which measure noise coherence, distribution, signal/noise ratios(SNR) and nonlinear interpolation.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.543-548
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• 2014

Multiple-model based speech recognition framework (MMSR) has been known to be very successful in speech recognition. Since it uses multiple hidden Markov modes (HMMs) that corresponds to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can have a close match with the test noisy speech. However, since the number of HMM sets is limited in practical use, the acoustic mismatch still remains as a problem. In this study, we experimentally determined the optimal SNR mapping between the test noisy speech and the HMM set to mitigate the mismatch between them. Improved performance was obtained by employing the SNR mapping instead of using the estimated SNR from the test noisy speech. When we applied the proposed method to the MMSR, the experimental results on the Aurora 2 database show that the relative word error rate reduction of 6.3% and 9.4% was achieved compared to a conventional MMSR and multi-condition training (MTR), respectively.