• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.616-618
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• 2002

본 논문에서는 음성인식 성능을 높이기 위한 기본적 단계인 음성과 비음성 부분의 경계를 추출하는 음성 경계 추출 방법을 제안한다. 음성경계 추출을 위한 특징들로는 시간영역 분할 파라미터인 ZCR, MA를 사용하고 주파수 영역 분할 파라미터로 주파수 대역 파워 에너지 (Frequency band power energy), 포만트 계수 (Formant coefficient)를 사용하였고 각 파라미터들을 이용하여 음성 경계를 결정할 때 경험에 의해 임계치를 결정하는 단점을 보안하기 위해서 신경망을 이용한다. 신경망의 가중치와 임계치들은 지도 학습을 통해 최적화 되고, 학습을 통해 구성된 망을 음성과 비음성의 경계치 구분에 사용한다.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.79-86
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• 2001

The CTD(Cumulative Trauma Disorder) as a new kind of occupational disease occurs mainly to workers on handling line under the highly-specialized industrial environments. This study took into account their exposure to Cumulative Trauma Disorders(CTD) by the utilization of EMG system, with respect to worker's muscle fatigue test according to fulfillment of iterative and simple task. The findings of this study were as follows : From the result of AEMG test analysis, worker's fatigue extent according to elapsed time of task was inclined to be increased continually. On the other hand, after its task ending, their fatigue extent was inclined to be decreased than before-circumstance of refractory brick lifting. The transference of MF(Median Frequency) and MPF(Mean Power Frequency) had highly significant difference between muscle fatigue and the elapsed time of work. Especially, their fatigue extent to erectorspinae and multifidus to lift firebrick was increased in the mean time. The transference of ZCR(Zero Crossing Rate) had considerable significant difference between muscle fatigue and the elapsed time of work. In short, as the work went of the muscle fatigue extent increased gradually. Thus, it can be concluded that the fatigue of erectorspinae and multifidus extent according to fulfillment of iterative and simple task is gradually being increased.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.31-36
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• 2011

In this paper, Mel-Frequency Cepstral Coefficient (MFCC), Decorrelated Filter Bank (DFB), Octave-based Spectral Contrast (OSC), Zero-Crossing Rate (ZCR), and Spectral Contract/Roll-Off are combined as a set of multiple feature-vectors for the music genre classification system based on the Support Vector Machine (SVM) classifier. In the conventional system, feature vectors for the entire genre classes are normalized for the SVM model training and classification. However, in this paper, selected feature vectors that are compared based on the One-Against-One (OAO) SVM classifier are only used for normalization. Using OSC as a single feature-vector and the multiple feature-vectors, we obtain the genre classification rates of 60.8% and 77.4%, respectively, with the conventional normalization method. Using the proposed normalization method, we obtain the increased classification rates by 8.2% and 3.3% for OSC and the multiple feature-vectors, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.4
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• pp.12-21
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• 2001

Recently, as work strength is deepened, as well, labor environments is changed, simple and iterative worker's Cumulative Trauma Disorders(CTD) is gradually being increased. Accordingly, this study was designed to represent its system design to carry out their iterative and simple task by machine through the difference of muscle fatigue between worker on handling line and worker under the work environments by Air Balance System for the purpose of analyzing their muscle fatigue test according to fulfillment of iterative and simple task. From the result of this study, with regard to the comparison of muscle fatigue between work on handling line and work on automation line on the occasion of refractory brick loading, their muscle fatigue extent under the work environments by Air Balance system was lower than it of handling by AMEG(64.1%), MF(65.3%), MPF(64.3%), ZCR(63.6%) respectively. And also, generally there showed similar transfer at the aspect of muscle mobilization. In other words, we can say that work environments by Air Balance System is beneficial at the aspect of alleviating works' fatigue extent on handling line. As well, the result of this study shows that worker's exposure to Cumulative Trauma Disorders(CTD) is relatively low.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.11-18
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• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.25-28
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• 2007

본 논문은 FM 라디오 방송의 오디오 신호를 블록 단위로 음성 음악을 판별하는 시스템을 제안하는 논문이다. 본 논문에서는 음성 음악 판별 시스템을 구축하기 위해 다양한 특정 파라미터와 분류 알고리즘을 제안 한다. 특정 파라미터는 신호처리 분야(Centroid, Rolloff, Flux, ZCR, Low Energy), 음성 인식 분야(LPC, MFCC), 음악 분석 분야(MPitch, Beat)에서 각각 사용되는 파라미터를 사용하였으며 분류 알고리즘으로는 패턴인식 분야(GMM, KNN, BP)와 퍼지 신경망(ANFIS)을 사용하였고, 거리 구현은 Mahalanobis 거리를 사용하였다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.15 no.6
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• pp.23-28
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• 1978

This paper describes a speech recognition system for ten isolated spoken digits. In this system, acoustic parameters such as zero crossing rate, log energy and three formant frequencies estimated by linear prediction method were extracted for classification and/or recognition purpose(s). The former two parameters were used for the classification of unvoiced consonants and the latter one for the recognition of vowels and voiced consonants. Promising recognition results were obtained in this experiment for ten digit utterances spoken by a male speaker.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.17-22
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• 2009

In this paper, we extract well-known parameters such as zero crossing rate(ZCR), low band energy(Band) and median frequency(MDF) from surface electromyogram (EMG), and compare to evaluate themselves as measures for fatigue. In experiments, 3 males and 3 females volunteered to participate in surface EMG recordings placed on the biceps brachii and each recording experiment continued until exhaustion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.1
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• pp.8-12
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• 2010

In this paper, we studied on AR model order decision for extraction of EMG median frequency by t-test and ANOVA and comparison of median frequency. And we extracted well-known parameters such as zero crossing rate(ZCR), low band energy(Band) and median frequency(MDF) from surface electromyogram (EMG). And we compared to evaluate themselves as measures for fatigue.