• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10C
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• pp.955-960
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• 2009

In this paper, we propose a novel approach to residual echo suppression (RES) algorithm based on tracking echo-presence uncertainty (TEPU) to improve the performance of acoustic echo suppression (AES) in the frequency domain. In the proposed method, the ratio of the microphone input and the echo-suppressed output signal power is employed as the threshold value for the decision rule to estimate the echo-presence uncertainty applied to the RES filter. The proposed RES scheme estimates the echo presence uncertainty in each frequency bin and effectively reduces residual echo signal in a simple fashion. The performance of the proposed algorithm is evaluated by the objective test and yields better results compared with the conventional schemes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1650-1658
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• 1998

This paper describes how to remove echoes effectively using speech parameter information provided form speech coder. More specially, the proposed adaptive echo canceller utilizes the excitation signal or linearly predicted error signal instead of output speech signal of vocoder as the input signal for adaptation algorithm. The normalized least mean ssquare(NLMS) algorithm is used for the adaptive echo canceller. The proposed algorithm showed a fast convergece charactersitcis in the sinulatio compared to the conventional method. Specially, the proposed echo canceller utilizing the excitation signal of speech coder showed about four times fast convergence speed over the echo canceller utilizing the output speech signal of the speech coder for the adaptation input.

• MALSORI
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• no.62
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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.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.42-46
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• 2002

In this paper, a new pre-processing algorithm is proposed to improve the performance of stereo acoustic echo canceller. The proposed algorithm has the improved performance by the estimation error reduction of filter coefficient using input signal which was reduced reverberation of room in the basis MINT (Mu1tip1e-input/output Inverse Theorem) filtering. For real stereo speech signal and real room impulse response the results of simulation, we showed that the proposed method could improved 3∼5 dB ERLE (Echo Return Loss Enhancement) regardless of NLMS (Normalized Least Mean Square) and Projection adaptive algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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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.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.483-490
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• 2009

In this paper, we develop a speech interface that has acoustic echo cancelling and barge-in functionalities in the car environment. In the echo canceller, DT (Double-Talk) detection algorithm using the correlation coefficients between reference and desired signals can make DT detection errors often in the background noise. We reduce the DT detection errors by using the average power of noise and echo estimated from the input signal. In addition, to make it possible for drivers to give speech command to the system by interrupting the speaker output, barge-in functionality is implemented with the combination of DT detection and appropriate gain control of the speaker output. Through the computer simulation with the assumed car environment and experiment in the real laboratory environment, implemented speech interface has shown good performance in removing acoustic echo signals in the noisy environment with proper operation of barge-in functionality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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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.