• 한국음향학회지
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• 제34권5호
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• pp.403-409
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• 2015

본 논문에서는 G.711 패킷 손실 은닉 알고리즘의 성능향상을 위해 적응적 신호 크기 예측을 사용하는 패킷 손실 알고리즘을 제안한다. 기존의 방법은 연속 손실이 발생하였을 때 20 %의 감쇠인자를 가지고 이득조절을 수행하였다. 그러나 이 방법은 신호의 변화를 고려하지 않기 때문에 신호가 왜곡되는 경우가 발생한다. 따라서 Least Mean Square(LMS) 예측기를 사용하여 이전과 이후 프레임의 정보를 통한 적응적 신호 크기 예측으로 이득을 조절하는 것을 제안한다. 제안된 알고리즘의 성능 평가는 Perceptual Evaluation of Speech Quality(PESQ) 평가를 통하여 나타내었다.

• 한국통신학회논문지
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• 제15권9호
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• pp.764-774
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• 1990

알고리즘 구조가 간단하고 실제적 응용이 쉬운 LMS 알고리즘을 이용한 적응 필터 설계에서 횡단필터, 격자필터 및 결합처리 격자필터의 예측기 특성을 비교 연구하였다. 실제적 설계를 위해 각 필터의 LMS 알고리즘의 이론적 배경과 구성을 보였고 최적 가중치 벡터와 반사계수의 적응 수렵조건을 명시하였으며 이를 컴퓨터 시뮬레이션을 통하여 나타내었다. 또한 정지및 비정지 입력 신호에 대한 이들 필터의 오차신호 특성과 잡음특성을 비교하였고 결합처리 격자필터가 이들 LMS 알고리즘을 이용한 적응 필터에서 가장 우수한 특성을 갖고 있음을 보였다.

• 한국대기환경학회지
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• 제2권3호
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• pp.52-56
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• 1986

In this paper a adaptive LMS(least mean-square) lattice predictor, which is composed of the adaptive lattice algorithm and LMS algorithm by Widrow-Hopf, is used to predict the future air pollution of the extraordinary levels in the environmental system. This prediction algorithm is applied to the one-step forward prediction of atmospheric CO concentration by using real observed data. Computer simulation proves that the power in the forward error sequences decreases as the number of stages in the lattice is increased.

• 디지털산업정보학회논문지
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• 제19권2호
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• pp.79-87
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• 2023

On-line learning has been adopted as a major educational method due to the COVID-19 pandemic. Students and faculties got accustomed to on-line educational environment as they experienced it during the COVID-19 pandemic. Development of various technologies and social requirement for educational renovation lay groundwork for on-line learning as well. Therefore, on-line learning or blended learning will be likely to go on after the end of COVID-19 pandemic and it is necessary to prepare the guidelines for effective utilizing on-line learning. The primary purpose of this study is to examine the learning behaviors and the learning effects by using LMS data. Learning behaviors were measured in terms of learning time and access frequency for pre-recorded video lectures targeting computer-practice classes. The results of empirical analysis reveal that frequency was the significant predictor of course achievements but learning time was not. The findings of empirical analysis will provide insights that the effective planning and designing on-line classes based on learning behaviors are key to enhancing learning effects and learner's satisfaction.

• 한국정보전자통신기술학회논문지
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• 제14권4호
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• pp.247-256
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• 2021

본 논문에서는 피치 하모닉 움직임 예측과 적응적 신호 크기 예측을 이용한 패킷 손실 알고리즘을 제안한다. 스펙트럼 움직임 예측 방법은 사용 가능한 이전 패킷의 스펙트럼 상의 움직임을 일정한 부대역으로 나누어 손실된 신호의 움직임을 예측하여 복원한다. 제안하는 알고리즘에서는 음성신호를 유성음과 무성음으로 구분하여 유성음의 경우 피치 주파수를 활용하여 피치 하모닉으로 나누어 손실된 신호의 피치 하모닉 움직임을 예측하여 복원하고 무성음의 경우 스펙트럼 움직임 예측 방법을 사용하여 신호를 복원한다. 음성 프레임의 연속 손실이 발생한 경우 LMS(Least Mean Square) 예측기를 사용하여 이전 프레임의 이득 정보를 활용하여 신호 크기를 예측하여 출력 신호의 이득을 조절하는 방법을 제안한다. 객관적 평가방법인 PESQ (Perceptual Evaluation of Speech Quality) 시험을 통해 제안된 알고리즘의 성능을 평가하였고 기존의 방법보다 MOS 0.1의 성능 개선을 보였다.

• 전자공학회논문지B
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• 제30B권12호
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• pp.12-20
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• 1993

In a conventional adaptive echo canceller, an ADF(Adaptive Digital Filter) with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to compute the coefficients, and NET detector using energy comparison method prevents the ADF to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yields more accurate detection of the start point of the NET signal.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1992년도 학술논문발표회 논문집 제11권 1호
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• pp.23-28
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• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1991년도 추계종합학술발표회논문집
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• pp.111-114
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• 1991

This paper proposes an efficient adaptive echo canceller using pilot filter approach to achieve improved convergence speed. The pilot filter is an adaptive filter with only a few filter coefficients to filter the received signal for the purpose of whitening the signal. Thus the convergence speed of the main LMS-TDL filter combined with the pilot filter is improved. In the proposed echo canceller, an adaptive lattice predictor as the pilot filter is used and its inverse filter is used to equalize the distorted near end talker signal. Simulation results for colored signal show that the convergence speed of the proposed echo cancellation algorithm is faster than that of the conventional LMS-TDL echo cancellation algorithm.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.437-442
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• 2000

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional FX-LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.941-943
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• 1995

The convergence of adaptive algorithm depends mainly on the proper choice of the design factor called the covergence factor. In the paper, an optimal convergence factor involved in TRLMS algorithm, which is used to predict the coefficients of the ARMA predictor in ADPCM is presented. It is shown that such an optimal value can be generated by system signals such that the adaptive filter becomes self optimizing in terms of the convergence factor. This algorithm is applied to real image.