• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.49-53
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• 1992

In many applications such as speech analysis, speech coding, speech recognition, etc., the voiced-unvoiced decision should be performed correctly for efficient processing. One of the parameters which are used for voice-unvoiced decision is zero-crossing. But the information of higher formants have not represented as the zero-crossing rate for higher formants of speech signals.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3166-3168
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• 2000

This paper proposed variable threshold dual rate ADPCM coding method which is modified from the standard ADPCM of ITU G.726 for speech quality improvement. The speech quality of variable threshold dual rate ADPCM is better than single rate ADPCM at noisy environment without increasing the complexity by using ZCR(Zero Crossing Rate). In this case, ZCR is used to divide input signal samples into two categories(noisy & speech). The samples with higher ZCR is categorized as the noisy region and the samples with lower ZCR is categorized as the speech region. Noisy region uses higher threshold value to be compressed by 16Kbps for reduced bit rates and the speech region uses lower threshold value to be compressed by 40Kbps for improved speech quality. Comparing with the conventional ADPCM, which adapts the fixed coding rate. the proposed variable threshold dual rate ADPCM coding method improves noise character without increasing the bit rate. For real time applications, ZCR calculation was considered as a simple method to obtain the background noise information for preprocess of speech analysis such as FFT and the experiment showed that the simple calculation of ZCR can be used without complexity increase. Dual rate ADPCM can decrease the amount of transferred data efficiently without increasing complexity nor reducing speech quality. Therefore result of this paper can be applied for real-time speech application such as the internet phone or VoIP.

• Speech Sciences
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• v.9 no.3
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• pp.35-45
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• 2002

A simple algorithm for discriminating voiced sounds in a speech is proposed. In addition to low-frequency energy and zero-crossing rate (ZCR), both of which have been widely used in the past for identifying voiced sounds, the proposed algorithm incorporates pitch variation to improve the discrimination rate. Based on TIMIT corpus, evaluation result shows an improvement of 13% in the discrimination of voiced phonemes over that of the traditional algorithm using only energy and ZCR.

• Korean Journal of Applied Biomechanics
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• v.16 no.3
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• pp.117-124
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• 2006

The modern convenient life formed by industrial development becomes lack of exercise and takes an interest in diet. Specially, professional walking shoes is developed as people take an interest in jogging, Those shoes, professional walking shoes or Dietshoes, increase exercise effects by change of heel types. Therefore, this study investigated motility effects by EMG experiment in order to measure Muscle Activities (MA) while wearing diet shoes (backless). Experiment was conducted by EMG measurement, from calf (gastrocnemius muscle), thigh (vastus muscle) and waist (erector spinae muscle), of 12 high school students. Exercise effects between the two shoes were analyzed by EMG (MF; Median Frequency, MPF; Mean Power Frequency, ZCR; Zero Crossing Rate). Results showed that the Dietshoes(MF: 48.21Hz, MPF: 65.0Hz, ZCR: 100.6Hz) had larger EMG value than that of Normal shoes(MF: 40.47Hz, MPF: 58.04Hz, ZCR: 82.09Hz). Also, in MA, the highest activities are showed in the calf, the second one is in waist, and last one is in thigh during gate. ANOVA between shoes in measurement parts showed significant effects in MF (gastrocnemius: p-value=.022, vastus laterals: p-value=.037, erector spinae: p-value=.082), MPF (gastrocnemius: p-value=.032, vastus laterals: p-value=.046, erector spinae: p-value=.090), and ZCR (gastrocnemius: p-value=.000, vastus laterals: p-value=.004, erector spinae: p-value=.134). And MA of Dietshoes is higher than that of Normal shoes, and decreasing rate of MA in Dietshoes is less than that of Normal shoes. Thus, this study validates exercise effects of Dietshoes.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.154-159
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• 2003

In this paper, we proposed variable threshold dual rate ADPCM coding method which adapts two coding rates of the standard ADPCM of ITU G.726 for speech quality improvement at a comparably low coding rates. The ZCR(Zero Crossing Rate) is computed for speecd data and under the noisy environment, noise data dominant region showed higher ZCR and speech data dominant region showed lower ZCR. The speech data with the higher ZCR is encoded by low coding rate for reduced coded data and the speech data with the lower ZCR is encoded by high coding rate for speech quality improvements. For coded data, 2 bits are assigned for low coding rate of 16[Kbps] and 5 bits are is assigned for high coding rate of 40[Kbps]. Through the simulation, the proposed idea is evaluated and shown that the variable dual rate ADPCM coding technique shows the qood speech quality at low coding rate.

• Proceedings of the Acoustical Society of Korea Conference
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• 1985.10a
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• pp.75-78
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• 1985

본 연구는 시간 영역의 parament를 이용하여 한국어 숫자음(영, 일, 이, 삼, 사, 오, 육, 칠, 팔, 구)을 인식했다. 입력 음성 신호 X(n)의 Beginning Point와 Ending point를 ZCR(Zero-crossing Rate), Magnitude, Energy, Autocorrelation을 이용 Beginning point와 Ending point를 구하고 자음부의 인식은 위 계수들을 이용하여 행했다. 또, 유성음 부분에서는 PARCOR(Partial Autocorrelation), LPC(Linear Predictive Coding)를 이용 모음부와 유성자음을 인식하여 모음을 6개 부류(ㅏ, ㅑ, ㅗ, ㅜ, ㅠ, ㅣ)로 구분 인식했다. 이 방법에 의하면 입력 음성 신호 X(n)의 B.P(Beginning Point)와 E.P(Ending Point)를 쉽게 추출 가능하며 또한 각 Parameter를 이용하여 94.4%의 인식율을 얻었다.

• ETRI Journal
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• v.43 no.1
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• pp.82-94
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• 2021

In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.67-72
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• 2013

Temperature is a critical parameter in yield improvement for wafer manufacturing. In chemical vapor deposition (CVD) equipment, crack defect in ceramic heater leads to yield reduction, however, there is no suitable ceramic heater fault detection system for conventional CVD equipment. This paper proposes a short-time zero-crossing rate based fault detection method for the ceramic heater in CVD equipment. The proposed method measures the output signal ($V_{pp}$) of RF filter and extracts the zero-crossing rate (ZCR) as feature vector. The extracted feature vectors have a discriminant power and Gaussian mixture model (GMM) based fault detection method can detect fault in ceramic heater. Experimental results, carried out by measured signals provided by a CVD equipment manufacturer, indicate that the proposed method detects effectively faults in various process conditions.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.727-734
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• 2002

A simple algorithm for discriminating voiced sounds in a speech is proposed in this paper. In addition to low-frequency energy and zero-crossing rate (ZCR), both of which have been widely used in the past for identifying voiced sounds, the proposed algorithm incorporates pitch variation to improve the discrimination rate. Based on TIMIT corpus, evaluation result shows an improvement of 13% in the discrimination of voiced phonemes over that of the traditional algorithm using only energy and ZCR.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.