• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2093-2099
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• 2011

Speech signal and car noise signal such as muffler noise are segregated from the one which has both signals mixed using statistical method. To classify speech signal from the other in segregated signals, FFT coefficients were obtained for all segments of a signal where each segment consists of 128 elements of a signal. For several coefficients of FFT corresponding to the low frequencies of a signal, autocovariances are calculated between coefficients of same order of all segments of a signal. Then they were averaged over autocovariances. Linear equation was eatablished for the those autocovariances using the linear regression method for each siganl. The coefficient of the slope of the line gives reference to compare and decide what the speech signal is. It is what this paper proposes. The results show it is very useful.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.10
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• pp.797-802
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• 2016

This paper proposes a noble voice recognition method based on an adaptive MFCC and deep learning for embedded systems. To enhance the recognition ratio of the proposed voice recognizer, ambient noise mixed into the voice signal has to be eliminated. However, noise filtering processes, which may damage voice data, diminishes the recognition ratio. In this paper, a filter has been designed for the frequency range within a voice signal, and imposed weights are used to reduce data deterioration. In addition, a deep learning algorithm, which does not require a database in the recognition algorithm, has been adapted for embedded systems, which inherently require small amounts of memory. The experimental results suggest that the proposed deep learning algorithm and HMM voice recognizer, utilizing the proposed adaptive MFCC algorithm, perform better than conventional MFCC algorithms in its recognition ratio within a noisy environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.553-558
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• 1999

The ECG signal is very important information for diagnosis of patient and a cardiac disorder That signal is hard to filter the noise because that is mixed with a lot of noise, and the error of the filtering will distort the ECG signal. The existing method for the filtering of the ECG signal has structure that has many steps for filtering, so that structure is complex and the processing speed is slow. For the improvement of that problem, we propose the method of filtering that has simple structure using the RBF neural networks and have good results.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.189-200
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• 2015

Stationary bootstrapping is applied to a Lagrangian multiplier (LM) test to test market microstructure noise (MMN) in financial asset prices. A Monte-Carlo experiment shows that the bootstrapping method improves the size of the original LM test which has some size distortion for conditional heteroscedastic models. The proposed test is illustrated for real data sets like KOSPI index and Won-Dollar exchange rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1659-1666
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• 2018

When the input sound is mixed voice and sound, it can be seen that the voice recognition rate is lowered due to the noise, and the speech recognition rate is improved by improving the MEMS device which is the H / W device in order to overcome the S/W processing limit. The MEMS microphone device is a device for inputting voice and is implemented in various shapes and used. Conventional MEMS microphones generally exhibit excellent performance, but in a special environment such as noise, there is a problem that the processing performance is deteriorated due to a mixture of voice and sound. To overcome these problems, we developed a newly designed MEMS device that can detect the voice characteristics of the initial input device.

• Journal of IKEEE
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• v.22 no.4
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• pp.1109-1114
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• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.10a
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• pp.102-104
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• 1983

This paper analyzes the patterns of Toneburst Waveform that generated Volumetarily in Broadband, as various Parameters, and applicate in the case that it is mixed Random Noise. As a result, it prooves that auto correlation function is Optimal parameter in analysis of Tone Burst wave form but reference signal.

• Journal of radiological science and technology
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• v.30 no.1
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• pp.1-6
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• 2007

This study is to decrease compound noise in x-ray films. This study compared Signal to noise ratio(SNR), Peak signal to noise ratio(PSNR), Mean square error(MSE) to surface of the earth. In addition, we evaluated noise elimination effect according to the kernel size of Median filter. This experiments show that some filters are useful by finding image that is near in circle image comparing circle picture with each processed picture. In noise power value, when cutoff frequency was compared with other filters of cutoff frequency. Cutoff frequency of $2/3\pi{\sim}3/4\pi$ is good and it shows good SNR and PSNR. Therefore, it can display high filter effect. As Median Filter's Kernel size grows SNR value gets bigger, which shows better filter effect. Most pictures are distorted after filter application in medical treatment image. It is important to keep spatial resolution in most medical images. Visual estimation as well as quantitative indicators should be necessary for a better image.

• Journal of IKEEE
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• v.13 no.4
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• pp.9-14
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• 2009

This paper describes measurement of an on-ship power supply noise in mixed-signal integrated circuits. To measure the on-chip power supply noise, we can check the effects of analog circuits and compensate it. This circuit consists of two independent measurement channels, each consisting of a sample and hold circuit and a frequency to digital converter which has a buffer and voltage controlled oscillator(VCO). The time-based voltage information and frequency-based power spectrum density(PSD) can be achieved by a simple analog to digital conversion scheme. The buffer works like a unit-gain buffer with a wide bandwidth and VCO has a high gain to improve resolution. This circuit was fabricated in a 0.18um CMOS technology and has 2.06mV/count. The noise measurement circuit consumes 15mW and occupies $0.768mm^2$.

• Journal of Audiology & Otology
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• v.25 no.3
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• pp.146-151
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• 2021

Background and Objectives: The relationship between hearing aid (HA) use and improvement in cognitive function is not fully known. This study aimed to determine whether HAs could recover temporal resolution or hearing in noise functions. Materials and Methods: We designed a prospective study with two groups: HA users and controls. Patients older than 45 years, with a pure tone average threshold of worse than 40 dB and a speech discrimination score better than 60% in both ears were eligible. Central auditory processing tests and hearing in noise tests (HINTs) were evaluated at the beginning of the study and 1, 3, 6, and 12 months after the use of a monaural HA in the HA group compared to the control group. The changes in the evaluation parameters were statistically analyzed using the linear mixed model. Results: A total of 26 participants (13 in the HA and 13 in the control group) were included in this study. The frequency (p<0.01) and duration test (p=0.02) scores showed significant improvements in the HA group after 1 year, while the HINT scores showed no significant change. Conclusions: After using an HA for one year, patients performed better on temporal resolution tests. No improvement was documented with regard to hearing in noise.