• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.38-44
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• 1999

In this paper, a new spectral subtraction technique with two microphone inputs is proposed. In conventional spectral subtraction using a single microphone, the averaged noise spectrum is subtracted from the observed short-time input spectrum. This results in reduction of mean value of noise spectrum only, the component varying around the mean value remaining intact. In the method proposed in this paper, the short-time noise spectrum excluding the speech component is estimated by introducing the blocking matrix used in Griffiths-Jim-type adaptive beamformer with two microphone inputs, combined with the spectral compensation technique. A simulation was conducted to verify the effectiveness of the method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9A
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• pp.678-683
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• 2003

The pseudo-noise (PN) code acquisition based on the sequential estimation (SE) proposed by Ward performs well only at relatively high chip signal-to-noise ratios (SNRs). In this paper, a seed accumulating sequential estimation (SASE) method and a PN code acquisition system based on it are proposed, which perform well at low chip SNR (of practical interest) also. Then, the mean acquisition time performance of the proposed system is investigated. Numerical results show that the system based on the SASE performs dramatically better than that based on the SE at low chip SNR, and the improvement becomes larger as the period of PN code increases.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.129-136
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• 2005

To reduce the noise in the noisy speech, it is desirable to change the parameters of the speech processing system according to the noise intensity to reproduce a good quality speech. This paper proposes an estimation method of noise intensity using a three layered neural network, which is able to learn the three graded speeches that is degraded by white noise or road noise. Experimental results demonstrate that the noise intensity could be estimated by the neural network. Even if the speakers and speech data are different from the training data, estimation rates for the noise intensity can be estimated by the neural network with an average accuracy of $95\%$ or more for white noise.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1924-1925
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• 2011

본 논문에서는 비선형 기동을 하는 기동표적의 추정된 잡음을 분석하여 표적의 기동패턴을 분석하는 알고리즘을 제시하고자 한다. 기동표적의 추정위치와 측정치에서 발생하는 잡음을 가속도와 순수 잡음으로 분리하고 분리된 성분을 분석하여 표적의 기동 패턴을 인식하고 동시에 추적을 실시하는 알고리즘을 구성한다. 잡음의 분리는 퍼지 클러스터링(FCM : Fuzzy C-means Clustering) 기법을 이용하여 적절한 추정값을 이용한다. 추정된 표적의 속도와 가속도, 잡음을 재 구성하여 기동표적의 기동패턴을 분석하고, 동시에 추적을 실시한다. 위의 과정을 통해 가속도를 분리한 후 비선형성을 지닌 기동표적의 기동패턴을 선형화 하여 칼만필터를 이용 잡음을 분리하고 가속도를 다시 보상하여 추적 알로리즘을 구성한다. 그리고 제안된 알고리즘의 수행 가능성을 보여 주기 위하여 몇 가지 예를 제시하였다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.829-832
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• 2001

본 논문에서는 2차 통계치를 이용하여 패널추징 및 등화 기법을 제안하였다. 기존의 채널 추정 알고리듬은 잡음이 없는 환경에서 LS방법을 이용하기 때문에 잡음이 강한 패널에서는 원하는 성능을 얻을 수 없는 단점이 있다. 수신신호의 상관행렬의 최소 고유값에 대응하는 고유벡터는 채널의 임펄스 응답에 관한 정보를 포함하고 있다. 이러한 고유 벡터를 매시간마다 갱신시키면서 구하는 적응 알고리듬을 제안하고 이를 이용하여 블라인드 채널 추정 및 등화기 파라미터를 추정하였다. 제안한 알고리듬은 잡음에 강인한 특성을 보일 뿐 아니라 기존의 알고리듬들 보다 우수한 채널 추정 및 등화 성능을 모의 실험을 통하여 검증하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.298-304
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• 2012

In this paper, we propose robust estimators for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise environments. We first propose a maximum-likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then, we present a simpler suboptimal estimator based on the ML estimator. From numerical results, it is demonstrated that the proposed estimators not only outperform the conventional estimators, but also have a robustness in non-Gaussian noise environments.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.14-17
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• 2003

웨이블릿 변환 영역에서 잡음제거 방법 중 Visushrink 추정에 사용되는 경계값은 측정 데이터 수와 잡음편차에 비례하는 것으로 알려져 있으나 잡음편차가 알려지지 않은 경우 Donoho는 웨이블릿 변환 영역의 최고대역에서 잡음편차 추정 방법을 제시하였다. 본 논문에서는 분산이 데이터 수에 반비례함을 이용하여 threshold 기법을 이용하여 잡음제거 시 계산량을 감소를 목적으로 차 신호를 이용하여 측정데이터 수를 줄인 후 영상신호의 가우시안 잡음을 soft threshold 기법을 적용하고 이 기법의 실용성을 밝혔다.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1061-1068
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• 2012

This paper deals with a noise reduction algorithm which uses the binary masking approach in the time-frequency domain to improve speech intelligibility. In the binary masking approach, the noise-corrupted speech is decomposed into time-frequency units. Noise-dominant time-frequency units are removed by setting the corresponding binary masks as "0"s and target-dominant units are retained untouched by assigning mask "1"s. We propose a binary mask estimation by comparing the local signal-to-noise ratio (SNR) to a threshold. The local SNR is estimated by a training-based approach. An optimal threshold is proposed, which is obtained from observing the distribution of the training database. The proposed method is evaluated by normal-hearing subjects and the intelligibility scores are computed by counting the number of words correctly recognized.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.404-406
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• 2021

In modern society, artificial intelligence and automation are being applied in various fields due to the development of the 4th industrial revolution and IoT technology. In particular, systems with a high proportion of image processing, such as automated processes, intelligent CCTV, medical industry, robots, and drones, are susceptible to external factors noise. In this paper, we propose a digital filter based on noise estimation and weights to reconstruct an image in a complex noise environment. The proposed algorithm classifies the types of noise using noise judgment, and determines the noise level of the filtering mask to switch the filtering process to obtain the final output. In order to verify the performance of the proposed algorithm, simulation was conducted, compared with the existing filter algorithm, and the results were analyzed.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.111-118
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• 2004

Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data because of the reasonable performance at the low complexity. The VisuShrink which removes the noise with the universal threshold is one of the techniques. The universal threshold is proportional to the noise deviation and the number of data samples. In general, because the noise deviation is not known, one needs to estimate the deviation for determining the value of the universal threshold. But, only for the finest scale wavelet coefficients, it has been known the way of estimating the noise deviation, so the noise in coarse scales cannot be removed with the VisuShrink. We propose here a new denoising method which removes the noise in each scale except the coarsest scale by Visushrink method. The noise deviation at each band is estimated by the monotonic transform and weighted deviation, the product of estimated noise deviation by the weight, is applied to the universal threshold. By making use of the universal threshold and the Soft-Threshold technique, the noise in each band is removed. The denoising characteristics of the proposed method is compared with that of the traditional VisuShrink and SureShrink method. The result showed that the proposed method is effective in denoising on Gaussian noise and quantization noise.