• Speech Sciences
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• v.9 no.1
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• pp.193-206
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• 2002

The aim of this study is to find out how the labiovelar onglide /w/ in English kwV sequences that have minimal pairs with kV sequences is pronounced differently among Korean EFL learners based on acoustic evidence. This study tries to identify /w/ sound in English kwV sequences through spectrograms and to examine the duration ratios of each segment in kwV words to compare the patterns of an English native speaker with those of Korean speakers of English. In spectrographic analyses, the complete deletion of /w/ and partial pronunciation of /w/ dubbed [$k^{w}$] were identified as well as the targetappropriate production of /w/. The general production patterns with respect to the duration ratios in English [kw] sequence words showed that the subjects who produced /w/ had similar ratio patterns that the native speaker had in that the vowel duration ratio in kwV sequences was shorter than that in kV sequences. By contrast, the subjects who deleted [w] had a long ratio of the onset [$k^{h}$] while the speaker with a partial pronunciation of /w/ had a long ratio of the following vowel.

• Speech Sciences
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• v.9 no.3
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• pp.3-16
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• 2002

We examined some problems in speaker identification procedures: transformation of acoustic parameters into auditory scales, invalid measurement values, and comparability of spectral energy values across the frequency range. To resolve those problems, we analyzed the acoustic spectral energy of three Korean numbers produced by ten female students from narrow-band spectrograms at 19 proportional time points of each voiced segment. Then, cells of the first five spectral matrices were averaged to form a matrix model for each speaker. The correlation coefficients and sum of the absolute amplitude difference in each pair of the spectral models of the ten subjects were obtained. Also, some individual matrix models were compared to those of the same subject or the other subject with a similar spectral model. Results showed that in numbers '2' and '9' subjects could not be clearly distinguished from the others but in number '4' it shed some possibility of setting threshold values for speaker identification if we employed the coefficients and the sum of absolute difference. Further studies would be desirable on various combinations of the range of long-term average spectra and the degree of signal pre-emphasis.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.1-6
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• 2020

There are various approaches to evaluate a watermelon sweetness. However, there are some limitations to evaluating cost, watermelon damage, and subjective issue. In this study, we developed a novel approach to predict a watermelon sweetness using reflected sound and the machine learning algorithm. It was observed that higher brix watermelon produced higher spectral power is reflected sound. Based on the spectral-temporal features of reflected sound, the machine learning algorithms could accurately predict the sweetness group at a rate of 83.2 and 59.6 % in 2-groups and 3-groups classification, respectively.

• Journal of Biomedical Engineering Research
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• v.7 no.2
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• pp.139-146
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• 1986

Pulsed ultrasonic Doppler system is a useful diagnostic instrument to measure blood-flow-velocity, velocity profile, and volume-blood-flow. This system is more powerful compare with 2-dimensional B-scan tissue image. A system has been deve- loped and ii being evaluated using TMS 32010 DSP. We use this DSP for the purpose of real-time spectrum analyzer to obtain spectrogram in singlegate pulsed Doppler system and for the serial comb filter to cancel clutter and zero crossing counter to estimate Doppler mean frequency in multigate pulsed Doppler system. The Doppler shift of the backscattered signals is sensed in a phase detector. This Doppler signal corresponds to the mean velocity over a some region in space defined by the ultrasonic beam dimensions, transmitted pulse duration, and transducer ban(iwidth. Multi- gate pulsed Doppler system enable the transcutaneous and simultaneous assessment of the velocities in a number of adjacent sample volumes as a continuous function of time. A multigate pulsed Doppler system processing the information originating from presented.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.240-247
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• 2006

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.316-324
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• 2020

It has experienced how difficult People with foreign language learning, it is to pronounce a new language different from the native language. The goal of various foreigners who want to learn Korean is to speak Korean as well as their native language to communicate smoothly. However, each native language's vocal habits also appear in Korean pronunciation, which prevents accurate information transmission. In this paper, the pronunciation of Chinese learners was compared with that of Korean. For comparison, the fundamental frequency and its variation of the speech signal were examined and the spectrogram was analyzed. The Formant frequencies known as the resonant frequency of the vocal tract were calculated. Based on these characteristics parameters, the classifier of the Supporting Vector Machine was found to classify the pronunciation of Koreans and the pronunciation of Chinese learners. In particular, the linguistic proposition was scientifically proved by examining the Korean pronunciation of /ㄹ/ that the Chinese people were not good at pronouncing.

• Speech Sciences
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• v.15 no.3
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• pp.39-51
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• 2008

Formant measurements are one of the most important factors to objectively test cross-linguistic differences among vowels produced by speakers of any given languages. However, many speech analysis softwares present erroneous estimates and some researchers use them without any verification procedures. The purposes of this paper are to examine formant measurements of complex waves which were synthesized from the average formant values of five Korean vowels using three default methods in Praat and to verify the measured values of the five vowels produced by 20 students using one of the methods. Variances along the time axis are discussed after determining absolute difference sum from the 1/3 vowel duration point. Results show that there were smaller measurement errors by the burg method. Also, greater errors were observed in the sl or lpc methods mostly caused by the inappropriate formant settings. Formant measurement deviations were greater in those vowels produced by the female students than those of the male students, which were mostly attributed to the settings for the vowels /o, u/. Formant settings can best be corrected by changing the number of formants to the number of visible dark bands on the spectrogram. Those results suggest that researchers should check the validity of the estimates from the speech analysis software. Further studies are recommended on the perception test of the original sound with the synthesized sound by the estimated formant values.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.287-294
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• 2019

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

• Korean Journal of Optics and Photonics
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• v.15 no.3
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• pp.278-284
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• 2004

A Second Harmonic Generation Frequency Resolved Optical Gating(SHG FROG) system was developed. Its performance test shows that it is capable of accurately measuring the temporal evolution of the electric field, both amplitude and phase, of femtosecond light pulses. For the retrieval of the temporal evolution of light pulses from their spectrograms obtained by using the FROG system, Principal Components Generalized Projection(PCGP) algorithm is used and in addition we used additional constraints of second-harmonic spectrum, marginals in frequency and time-delay of the spectrogram. Such modification of the software brings about significant improvement in speed and stability of the pulse retrieval process.