• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2374-2379
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• 2011

The fundamental frequency of the voiced speech is estimated using the instantaneous frequency based on the symmetric higher order differential energy operator. The instantaneous frequency based on the symmetric higher order energy operator shows better frequency estimation result since it is aligned to the time instance of the signal. The speech is pre-processed by a lowpass filter to remove higher frequency components. Then, it is processed by the instantaneous frequency to obtain the fundamental frequency estimates. The symmetric higher order energy operator has been used as an indicator to determine the voiced/unvoiced speech. The fundamental frequency estimates are further processed by a moving average filter to obtain the monotonically changed estimates. The obtained fundamental frequency estimates have been compared with the spectrogram of the speech to confirm its accuracy.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.255-262
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• 2006

Frequency spectrum using the conventional Fourier analysis gives adequate information about the dynamic characteristics of the slider air bearing for the linear and stationary cases. The intermittent contacts for the extremely low flying height, however, generate nonlinear and nonstationary vibration at the instant of contact. Nonlinear dynamic model should be developed to simulate the impulse response of the air bearing during slider-disk contact. Time-frequency analysis is widely used to investigate the nonstationary signal. Several time-frequency analysis methods are employed and compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk. The representative Wigner-Ville distribution leads to the severe interference problem by cross terms even though it gives good resolution both in time and frequency. The smoothing process improves the interference problem at the expense of resolution. In order to get the results with good resolution and little interference, the reassignment method is proposed. Among others the reassigned Gabor spectrogram shows the best resolution and readability with negligible interference.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.687-696
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• 2019

Conventional feature of seismic classification focuses on strong seismic classification, while it is not suitable for classifying micro-seismic waves. We propose a feature extraction method based on histogram and Principal Component Analysis (PCA) in frequency-time space suitable for classifying seismic waves including strong, micro, and artificial seismic waves, as well as noise classification. The proposed method essentially employs histogram and PCA based features by concatenating the frequency and time information for binary classification which consist strong-micro-artificial/noise and micro/noise and micro/artificial seismic waves. Based on the recent earthquake data from 2017 to 2018, effectiveness of the proposed feature extraction method is demonstrated by comparing it with existing methods.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.819-820
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• 2006

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. To extract features, first we computed 6 indices to find out the information of 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 recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness. We have shown that proposed method was effective for detecting the arousal events.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of Sasang Constitutional Medicine
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• v.11 no.1
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• pp.137-157
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• 1999

The purpose of this study is to help classifying Sasang Constitution through correlation with sound characteristic. This study was done it under the suppose that Sasang Constitution has correlation with sound spectrogram. The following result were obtained about correlation between sound spectrogram and Sasang Constitution by comparison and analysis 1. Soeumin answered his voice low tone, smooth and quiet in the survey. Soyangin answered his voice high, clear, fast and speaking random. Taeumin answered his voice low, thick and muddy. 2. Taeyangin was significantly slow compared with the others in the time of reading composition. Taeyangin was significantly slow compared with the others in Formant frequency 1. Taeyangin was significantly discriminated from Soeumin in Formant frequency 5. Taeyangin was significantly low compared with the others in Bandwidth 2. Soeumln was significantly low compared with Taeyangin in Pitch Maximum and Pitch Maximum-Pitch Minimum. Taeyangin was significantly high compared with the others in Energy mean. 3. In list of specification, the discrimination rate was higher than that by lists of 13 in the results of Multi-dimensional 4-class minimum-distance. The discrimination rate of three disposition except Soyangin was higher than that of four disposition in the results of One way ANOVA and Analysis of dis crimination in SPSS/PC+. In CART, the estimate rate of Sasang Constitution discrimination was higher than any other method. It is considered that there is a correlation between sound spectrogram and Sasang constitution according to the results. And method of Sasang constitution classification through sound spectrogram analysis can be one method as assistant for the objectification of Sasang constitution classification.