• 한국통신학회논문지
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• 제34권10C호
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• pp.955-960
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• 2009

본 논문에서는 주파수영역에서 음향학적 반향 억제 (AES, acoustic echo suppression) 성능을 개선시키기 위해 tracking echo-presence uncertainty (TEPU) 기법에 근거한 새로운 잔여 반향 억제 (RES, residual echo suppression) 알고리즘을 제안한다. 제안된 방법은 RES를 위해 마이크로폰 입력신호 대 원단의 반향신호가 제거된 결과신호의 전력 비 (ratio)를 문턱 (threshold) 값에 의한 decision rule에 적용하여 추정된 echo-presence uncertainty를 RES 필터로 이용한다. 제안된 알고리즘은 각각의 주파수 채널에서 echo-presence uncertainty를 도출하여 용이하게 잔여 반향신호를 제거하는 장점을 가진다. 실제로 잔여 반향신호를 제거하기 위한 기존의 방법과 객관적인 실험을 통해 비교 평가한 결과 우수한 성능을 보였다.

• 한국통신학회논문지
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• 제23권7호
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• pp.1650-1658
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• 1998

본 논문에서는 이동통신 시스템의 반향을 제거하기 위해 음성부호화기에서 얻은 음성 분석 정보를 이용하여 반향을 제거하는 방법을 제시하였다. 반향 제거기 적응 알고리즘의 입력 신호로서 기존의 방법인 음성부호화기의 출력 음성신호를 사용하지 않고 음성 부호화기 디코더 과정에서 제공되어지는 여기 신호, 선형 예측 오차 신호를 사용하였다. 모의 실험을 위해 Normalized Least Mean Square(NLMS) 알고리즘을 이용한 적응 반향 제거기를 구성하였고, 기존의 음성신호를 사용하는 반향제거기에 비해 음성 부호화기에서 제공되어지는 음성의 여기 신호 성분을 적응 알고리즘 입력신호로 사용함으로써 40 dB Echo Return Loss Enhancement(ERLE)를 얻는데 걸리는 시간에 있어서 약 4배 정도의 빠른 속도를 얻을 수 있다.

• 대한음성학회지:말소리
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• 제62호
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• pp.85-96
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• 2007

In this paper, we carried out recognition experiments for noisy speech having various levels of car noise and output of an audio system using the speech interface. The speech interface consists of three parts: pre-processing, acoustic echo canceller, post-processing. First, a high pass filter is employed as a pre-processing part to remove some engine noises. Then, an echo canceller implemented by using an FIR-type filter with an NLMS adaptive algorithm is used to remove the music or speech coming from the audio system in a car. As a last part, the MMSE-STSA based speech enhancement method is applied to the out of the echo canceller to remove the residual noise further. For recognition experiments, we generated test signals by adding music to the car noisy speech from Aurora 2 database. The HTK-based continuous HMM system is constructed for a recognition system. Experimental results show that the proposed speech interface is very promising for robust speech recognition in a noisy car environment.

• 한국음향학회지
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• 제21권1호
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• pp.42-46
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• 2002

본 논문에서는 스테레오 음향 반향 제거기의 성능을 향상시킬 수 있는 새로운 전처리 방법의 반향 제거기를 제안한다. 제안한 반향 제거기는 MINT (Multiple input/output INverse Theorem) 필터링에 의해 실내 전달함수의 잔향이 저감되어진 입력을 사용함으로써 필터계수의 추정오차를 감소시켜 성능을 향상시킬 수 있었다. 실제의 스테레오 음성과 실제 음장의 전달함수를 사용한 시뮬레이션 결과, 제안한 방법이 NLMS (Normalized Least Mean Square)와 Projection 등의 적응 알고리즘 종류에 관계없이 ERLE가 3∼5 dB 향상됨을 확인하였다.

• 전기학회논문지
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• 제56권3호
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• pp.629-631
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• 2007

In hands-free full-duplex communication systems, acoustic signals picked up by the microphones can be mixed with echo signals as well as noises, which may result in poor performance of the corresponding communication system. Also, the system performance may decrease further if the reverberation occurs since it is harder to estimate the impulse response of the demixing system. For blind source separation (BSS) in such cases, a time-frequency masking approach can be employed to separate undesired echo signals and noises, but, permutation ambiguities also should be solved for the echo cancellation. In this paper, we propose a new acoustic echo cancellation (AEC) approach utilizing the time-frequency masking and higher-order statistics, whereby a desired signal selection, based on coherence and third-order statistics (i.e., kurtosis), is introduced along with output signal normalization. Simulation results demonstrate that the proposed approach yields better echo and noise cancellation performances than the conventional AEC approaches.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.645-651
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• 2003

The system in which a microphone and a loudspeaker are simultaneously used can cause an echo. The echo is caused by feedback between the output of the loudspeaker and the input of the microphone. The acoustic echo canceller is a device to cancel the echo in a communication system. Its general procedure for cancellation is first estimating the plant response of the feedback path and then eliminating the feedback signal from the input signal. In this paper, full-band and sub-band approaches are compared by using some simulation examples.

• 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.

• 전기학회논문지
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• 제64권6호
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• 한국음향학회지
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• 제28권5호
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• pp.483-490
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• 2009

본 논문에서는 배경잡음과 반향이 존재하는 차량환경에서 음성인식 성능을 향상시키기 위해 상관계수를 이용한 동시통화 검출 알고리즘을 적용한 음향 반향제거기와 barge-in 기능을 갖는 음성 인터페이스를 구현하였다. 상관계수를 이용한 동시통화 검출 알고리즘은 임계치 설정 및 배경잡음의 영향 등으로 인해 검출 오류가 발생한다. 이를 보완하기 위해 동시통화 검출 조건으로 매 샘플마다 입력신호에서 추정한 배경잡음 및 반향신호의 평균 전력을 이용하여 동시통화 검출 오류를 줄였으며, 시변의 임계치를 적용한 후처리 단을 통해 시변의 잔여 잡음 성분을 제거하였다. 또한 안내음성 중에 음성입력이 가능하도록 barge-in 기능을 적용한 음성 인터페이스 시스템을 구현하였다. 제안한 음성 인터페이스 시스템은 동시통화 검출 오류와 이로 인해 발생되는 문제점을 효율적으로 해결할 수 있음을 실험을 통하여 확인하였다.

• 전기학회논문지
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• 제63권5호
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.