• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2448-2450
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• 1998

For the exact classification of the arm motion this paper proposes EMG pattern recognition method with neural network. For this autoregressive coefficient, linear cepstrum coefficient, and adaptive cepstrum coefficient are selected for the feature parameter of EMG signal, and they are extracted from time series EMG signal. For the function recognition of the feature parameter a radial basis function network, a field of neural network is designed. For the improvement of recognition rate, a number of radial basis function network are combined in parallel, comparing with a backpropagation neural network an existing method.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.329-331
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• 1994

In this paper, we present a speaker-dependent Korean Isolated digit recognition system. At the preprocessing step, LPC cepstral coefficients are extracted from speech signal, and are used as the input of a Frequency Sensitive Competitive Learning(FSCL) neural network. We carried out the postprocessing based on the winning-neuron histogram. Experimetal results Indicate the possibility of commercial auto-dial telephones.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.148-154
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• 2021

With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.6
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• pp.671-676
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• 2011

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.215-216
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• 2012

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.63-72
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• 2014

The current study assessed the utility of acoustic analyses the most commonly used in routine clinical voice assessment including perturbation, nonlinear dynamic analysis, and Spectral/Cepstrum analysis based on signal typing of dysphonic voices and investigated their applicability of clinical acoustic analysis methods. A total of 70 dysphonic voice samples were classified with signal typing using narrowband spectrogram. Traditional parameters of %jitter, %shimmer, and signal-to-noise ratio were calculated for the signals using TF32 and correlation dimension(D2) of nonlinear dynamic parameter and spectral/cepstral measures including mean CPP, CPP_sd, CPPf0, CPPf0_sd, L/H ratio, and L/H ratio_sd were also calculated with ADSV(Analysis of Dysphonia in Speech and VoiceTM). Auditory perceptual analysis was performed by two blinded speech-language pathologists with GRBAS. The results showed that nearly periodic Type 1 signals were all functional dysphonia and Type 4 signals were comprised of neurogenic and organic voice disorders. Only Type 1 voice signals were reliable for perturbation analysis in this study. Significant signal typing-related differences were found in all acoustic and auditory-perceptual measures. SNR, CPP, L/H ratio values for Type 4 were significantly lower than those of other voice signals and significant higher %jitter, %shimmer were observed in Type 4 voice signals(p<.001). Additionally, with increase of signal type, D2 values significantly increased and more complex and nonlinear patterns were represented. Nevertheless, voice signals with highly noise component associated with breathiness were not able to obtain D2. In particular, CPP, was highly sensitive with voice quality 'G', 'R', 'B' than any other acoustic measures. Thus, Spectral and cepstral analyses may be applied for more severe dysphonic voices such as Type 4 signals and CPP can be more accurate and predictive acoustic marker in measuring voice quality and severity in dysphonia.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.96-107
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• 1996

A voice personality transformation algorithm using orthogonal vector space conversion is proposed in this paper. Voice personality transformation is the process of changing one person's acoustic features (source) to those of another person (target). In this paper, personality transformation is achieved by changing the LPC cepstrum coefficients, excitation spectrum and pitch contour. An orthogonal vector space conversion technique is proposed to transform the LPC cepstrum coefficients. The LPC cepstrum transformation is implemented by principle component decomposition by applying the Karhunen-Loeve transformation and minimum mean-square error coordinate transformation(MSECT). Additionally, we propose a pitch contour modification method to transform the prosodic characteristics of any speaker. To do this, reference pitch patterns for source and target speaker are firstly built up, and speaker's one. The experimental results show the effectiveness of the proposed algorithm in both subjective and objective evaluations.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.28 no.2
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• pp.84-88
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• 2017

Background and Objectives : The purpose of this study was to compare the usefulness of Cepstral peak prominence (CPP) with parameter of Multiple Dimensional Voice Program (MDVP) in evaluating unilateral vocal fold paraylsis patients with subjective voice impairment. Materials and Methods : From July 2014 to August 2016, 37 patients with unilateral vocal fold paralysis who had been diagnosed with unilateral vocal fold paralysis and had received two or more voice tests before and after the diagnosis were evaluated for maximum phonation time (MPT), MDVP and CPP. Respectively. Voice tests were performed with short vowel /a/ and paragraph reading. Results : The CPP-a (CPP with vowel /a/) and CPP-s (CPP with paragraph reading) of the Cepstrum were statistically negatively correlated with G, R, B, and A before the voice therapy. Jitter, Shimmer, and NHR of MDVP were positively correlated with G, R, B. Jitter, Shimmer, and NHR of the MDVP were significantly correlated with the Cepstrum index. G, B, A and CPP-a and CPP-s showed a statistically significant negative correlation and a somewhat higher correlation coefficient between 0.5 and 0.78. On the other hand, in MDVP index, there was a positive correlation with G and B only with Jitter of 0.4. Conclusion : CPP can be an important evaluation tool in the evaluation of speech in the unilateral vocal cord paralysis when speech energy changes or the cycle is not constant during speech.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.298-303
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• 2023

This study aimed to investigate the clinical utility of voiced sentence tasks for voice evaluation. To this end, we analyzed the correlation between perturbation-based acoustic measurements [jitter percent (jitter), shimmer percent (shimmer), Noise to Harmonic Ratio (NHR)] using sustained vowel phonation, and cepstrum-based acoustic measurements [Cepstral Peak Prominence (CPP), Low/High spectral ratio (L/H ratio)] using voiced sentences. As a result of analyzing data collected from 65 patients with voice disorders, there was a significant correlation between the CPP and jitter (r = -.624, p = .000), shimmer (r = -.530, p = .000), NHR (r = -.469, p = .000).This suggests that the cepstrum measurement of voiced sentences can be used as an alternative to the analysis limitations of the pathological voice such as not possible perturbation-based acoustic measurement, and result difference according to the analysis section.