• 한국음향학회:학술대회논문집
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• 한국음향학회 1992년도 학술논문발표회 논문집 제11권 1호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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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.

• 음성과학
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• 제9권3호
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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.

• 한국운동역학회지
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• 제16권3호
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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.

• 조명전기설비학회논문지
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• 제17권6호
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• pp.154-159
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• 2003

본 논문에서는 ITU G.726 규격을 만족하는 표준형 ADPCM 부호화 법을 이용하여 배경잡음의 크기에 따라 음성의 부호화율이 두가지로 가변하도록 함으로써, 낮은 데이터 전송률을 가지고도 단일 부호화율의 경우보다 개선된 음질을 갖는 부호화 기법을 제안하였다. 이를 위하여 배경잡음보다 큰 음성신호에 대하여는 데이터의 양이 커지더라도 음질을 향상시키기 위하여 40 [Kbps]로 압축하고, 작은 음성신호에 대하여는 16[Kbps]로 압축하여 데이터의 양을 줄이도록 하여 전체적으로 압축데이터의 양을 줄이면서 음질을 개선하도록 하였다. 입력된 음성신호에 대하여 두가지 압축율을 결정하기 위하여 영교차율(ZCR)을 사용하여 처리속도를 빠르도록 하였다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1985년도 학술발표회 논문집
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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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• 제43권1호
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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.

• 반도체디스플레이기술학회지
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• 제12권2호
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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.

• 한국음향학회지
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• 제21권8호
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• pp.727-734
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• 2002

본 논문에서는 유ㆍ무성음 구간을 검출하기 위한 간단한 알고리즘을 제안한다. 제안된 방법은 음성의 유ㆍ무성음의 주기성에 대한 특성을 보완할 수 있는 저대역 에너지와 영교차율, 그리고 주기성의 안정성을 판단하기 위한 피치 변화량을 파라미터로 사용하였다. 유ㆍ무성음의 구간검출을 음소단위의 검출이라는 측면에서 접근하여 음소군의 검출율과 음소군내의 음소의 검출율을 얻었다. TIMIT코퍼스 (corpus)를 데이터베이스로 사용하여 실험했을 때 유성음 음소 검출율이 약 13% 향상되었다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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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.