• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.43-50
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• 2016

This paper is on an acoustic echo canceller solving the double-talk problem by using the blind signal separation technology. The acoustic echo canceller may be deteriorated or diverged during the double-talk period. So we use the blind signal separation to detect the double talking by separating the near-end speech signal from the mixed microphone signal. The blind signal separation extracts the near-end signal from dual microphones by the iterative computations using the 2nd order statistical character in the closed reverberation environment. By this method, the acoustic echo canceller operates irrespective of the double-talking. We verified performances of the proposed acoustic echo canceller in the computer simulations. The results show that the acoustic echo canceller with this algorithm detects the double-talk periods well, and then operates stably without diverging of the coefficients after ending the double-talking. The merits are in the simplicity and stability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.413-420
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• 2011

This paper proposes a new adaptive algorithm to cancel the acoustic feedback and noise signals in the binaural hearing aids. The convergence performances of the proposed algorithm are improved by updating coefficients of the feedback canceller after the speech signal is cancelled from the residual signal with dual microphones. The feedback canceller firstly cancels the feedback signal from the microphone signal, and then the noise canceller reduces the noise by the beamforming method. To assure that binaural hearing aids converge stably, the left-sided hearing aid only is converged firstly, next the right-sided hearing aid only is converged. To verify performances of the proposed algorithm, simulations were carried out for a speech. As the results of simulations, it was proved that we can advance 14.43dB SFR(Signal to Feedback Ratio) on the average for the feedback canceller, 10.19dB SNR(Signal to Noise Ratio) improvement on the average for the noise canceller, in case that this algorithm is used.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.79-87
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• 2014

In this paper, we implemented the HearCAM platform with Raspberry PI B+ model which is an open source platform. Raspberry PI B+ model consists of dual step-down (buck) power supply with polarity protection circuit and hot-swap protection, Broadcom SoC BCM2835 running at 700MHz, 512MB RAM solered on top of the Broadcom chip, and PI camera serial connector. In this paper, we used the Google speech recognition engine for recognizing the voice characteristics, and implemented the pattern matching with OpenCV software, and extended the functionality of speech ability with SVOX TTS(Text-to-speech) as the matching result talking to the microphone of users. And therefore we implemented the functions of the HearCAM for identifying the voice and pattern characteristics of target image scanning with PI camera with gathering the temperature sensor data under IoT environment. we implemented the speech recognition, pattern matching, and temperature sensor data logging with Wi-Fi wireless communication. And then we directly designed and made the shape of HearCAM with 3D printing technology.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1330-1336
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• 2015

It is necessary to study on acoustic and vibratory response of a MEMS resonant accelerometer before applying to military applications. In this paper, we analyze why the resonant accelerometer reacts to an acoustic wave and a high frequency vibration. And we describe experimental results on acoustic and vibratory response of the accelerometer. The accelerometer consists of a proof mass and a dual ended tuning fork. It is a differential resonant accelerometer with arranging a pair of accelerometers. The mode shape was analyzed to find out the input mode frequency by using a FEM simulation. Some experiments regarding the acoustic noise was carried out by using a tweeter and a microphone in the anechoic room. Results showed that the accelerometer reacted to the acoustic wave and vibration which had the input mode frequency as we had expected. We showed experimentally not only that the susceptibility of the accelerometer to an acoustic wave was 70 dB but also that the effectiveness of applying an acoustic absorber and a metal case was 20 dB, respectively. Also, we could minimize the vibratory response property of the accelerometer by installing a IMU with a silicone rubber mount pad.