• Journal of the Semiconductor & Display Technology
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• v.17 no.3
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• pp.108-112
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• 2018

In this paper, a respiration measurement method using FMCW signal for off-the-shelf smartphone is presented and investigated. The proposed algorithm transmits FMCW signal periodically instead of transmitting continuously so that one can reduce the power consumption from speaker in smartphone and the algorithm complexity. In order to eliminate the clicking noise generated when transmitting FMCW signal, Tukey window with ${\alpha}=0.01$ is applied to prevent the noise from being heard. An application program for Android OS which can transmit FMCW signal through speaker and record the reflected signals through MIC has been developed. Since the total duration of the signal transmission is set to 20msec per 1 second for the experiments, the power consumption can be decreased by 80% compared to the continuous transmission. It was confirmed that the clicking noise is inaudible as long as a smartphone is located at more than 10cm from ears. In the experiments on a sleeping child, the breathing signal of about 0.27Hz was measured.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.728-732
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• 2010

GIS(Gas insulated switching gear) is power equipment with excellent dielectric strength and is economy merit in high confidence and stability. Recently, because equipment of GIS was occurring problem of confidence used for a long time, partial discharge on-line diagnosis systems have been importantly recognized. Partial discharge (PD) detection is an effective means for monitoring and evaluation of dielectric condition of gas insulated system (GIS). The ultra-high-frequency (UHF) PD detection technique can detect and locate the PD sources inside GIS by detecting electromagnetic wave emitted from PD source. Therefore, real-time diagnostic system using UHF detection method has been developed for this application is being expanded gradually. However, the signal of partial discharge occurring in SF6 gas is very weak and susceptible to external noises which mainly consist of PD in air. Thus, it is important to distinguish the PD in SF6 gas more sensitively from the external noises. Unfortunately, these external noise signals and the partial discharge signals have very similar characteristics. Therefore, to solve this problem, we need the signal processing method for distinguish partial discharge signals with external noise signals for improvement of SNR(signal to noise ratio) and sensitivity. In this paper, we proposed internal signal processing method for removing external noise signals with built-in pre.amplifier and frequency conversion circuit.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1279-1286
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• 2004

This paper continues companion paper, part I : measurement and analysis. As shown in companion Paper, information and energy in monaural signal is quite different from that of binaural signal. In this paper, difference between monaural and binaural signal of transportation noise are investigated in subjective response test. We executed hearing screening test before giving a subject response test and excluded subjects who had physical hearing loss. An annoyance response test was conducted using headphone to avoid cross-talk effect in binaural testing. Percentage of highly annoyed under binaural signal reproduction is higher than percentage of highly annoyed under monaural signal reproduction. Result implies binaural reproduction technique is proper for a study of human response to short-term noise exposure in a headphone simulated-environment.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.903-912
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• 2007

Faults of rotating parts of a train normally generate unexpected frequency band or impulsive sound[1] which has a period when it moves with a constant speed. The former can be detected by the moving frame acoustic holography method, which visualizes sound field that is generated by a moving and emitting pure tone or band limited noise source. We have attempted to apply the method to the latter case: the periodic impulsive sound which generate different signal compared with what can be measured by the band limited noise. The signal to noise ratio which determines the success of early fault detection must also be studied with the impulsive and moving signal. This research shows how the problems related with these issues can be resolved. The main idea is that periodic impulsive signal can be expressed by infinite set of discrete pure tones. This enables us to obtain lots of holograms that visualize periodic impulsive sound field including noise by using the moving frame acoustic holography method. Therefore holograms can be averaged to improve the signal to noise ratio until having reliable information that exhibits where the impulsive sources are. Theory and experiment by using the miniature vehicle are described [Work supported by BK21 & KRRI].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.663-668
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• 2008

In this paper, we analyzed the error rate Performance of convolution coded 16 QAM signal in impulsive noise Environments. We used convolution rode and maximum ratio combining diversity for performance improvement. We analyzed the error rate performance of 16 QAM signal in implusive noise environments compared with gaussian noise environments. As a result of analysis, there is a BER segment where the efficiency of system does not improve until which limit to raise a signal power potential from impulsive noise environment when the signal power potential which goes over this limit is supplied, BER efficiency improve much more.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.3
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• pp.117-122
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• 2001

The shock test of shipboard equipments is performed for the evaluation of the shock-resistant. capability by analyzing the maximum acceleration, the effective time duration and the shock response spectrum, etc. But some measured signals have impulsive noise and gaussian white noise because of the ambient noise, the acquisition equipment error and the transient movement of cables during the shock test. The improved transient signal analysis method which removes the noise of measured signal using the threshold policy of the median filter and the orthogonal wavelet coefficients is proposed. It was verified that the signal-to-noise ratio was improved about 30dB by the numerical simulation. And the shock response spectrum was extracted using the denoised shock response signal which was applied by this proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.222-228
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• 2010

In this paper, we propose a new approach to noise estimation incorporating spectral deviation with soft decision scheme to enhance the intelligibility of the degraded speech signal in non-stationary noisy environments. Since the conventional noise estimation technique based on soft decision scheme estimates and updates the noise power spectrum using a fixed smoothing parameter which was assumed in stationary noisy environments, it is difficult to obtain the robust estimates of noise power spectrum in non-stationary noisy environments that spectral characteristics of noise signal such as restaurant constantly change. In this paper, once we first classify the stationary noise and non-stationary noise environments based on the analysis of spectral deviation of noise signal, we adaptively estimate and update the noise power spectrum according to the classified noise types. The performances of the proposed algorithm are evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various ambient noise environments and show better performances compared with the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.81-84
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• 2001

We propose a novel noise littering method based on adaptive windowing. To restore a noisy signal adaptive filtering methods have been widely researched and used. However, conventional adaptive filtering methods have a trade-off between noise suppression and edge preservation since they adopt fixed size filters. In this paper applying the adaptive windowing concept to adaptive filtering, we overcome the trade-off, The filter size is adaptively selected depending on signal statistics. The visual results of the signal and image restorations convincingly show the superior preservation of edge and detail and suppression of noise for the proposed adaptive windowed adaptive filter compared with conventional methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1293-1298
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• 2013

This paper proposes a noise reduction algorithm using Wiener filter to remove the noise components from the noisy speech in order to improve the speech signal. The proposed algorithm first removes the noise spectrums of white noise from the noisy signal based on the noise reshaping and reduction method at each frame. And this algorithm enhances the speech signal using Wiener filter based on linear predictive coding analysis. In this experiment, experimental results of the proposed algorithm demonstrate using the speech and noise data by Japanese male speaker. Based on measuring the spectral distortion (SD) measure, experiments confirm that the proposed algorithm is effective for the speech by contaminated white noise. From the experiments, the maximum improvement in the output SD values was 4.94 dB better for white noise compared with former Wiener filter.