• 한국통신학회논문지
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• 제11권4호
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• pp.287-295
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• 1986

본 논문에서는 디지털 이동 무선통신을 위한 음성신호와 부호화 기법으로 SBC(sub-bnad coding)를 제안하고, SBC의 레일리(Rayleigh) 페이딩 채널에서의 음질의 강인성을 컴퓨터 시뮬레이션을 통해 조사하였다. 먼저 레일리 페이딩 채널, 시뮬레이터 및 16-ary DPSK(differential phase shift key) 수신기 모델을 제시한 후, 모델의 타당성을 이론치와 비교하여 입증하였다. 채널에러에 대한 영향은 SNR, LPC(linear predictive codin) 거리척도 및 주관적인 청각조사를 통해 검토하였다. BER(bit error rate) =$10_{-3}$, $10_{-2}$, 5$ imes$$10_{-2}$에 대한 시뮬레이션결과 BER=$10_{-2}$에서도 음성의 이해도는 확인되었으며, BER=5$ imes$$10_{-2}$에서도 음성통신에 사용하기는 충분하였다. 따라서 SBC는 ECC(error correction code) 사용없이 BER=$10_{-4}$~$10_{-2}$정도의 레일리 페이딩 채널에서 디지탈 이동무선통신에 응용이 가능함을 알 수 있었다.

• The Journal of the Acoustical Society of Korea
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• 제17권2E호
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• pp.31-37
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• 1998

This paper proposes a feature extraction method using wavelet transform for speech recognition. Speech recognition system generally carries out the recognition task based on speech features which are usually obtained via time-frequency representations such as Short-Time Fourier Transform (STFT) and Linear Predictive Coding(LPC). In some respects these methods may not be suitable for representing highly complex speech characteristics. They map the speech features with same may not frequency resolutions at all frequencies. Wavelet transform overcomes some of these limitations. Wavelet transform captures signal with fine time resolutions at high frequencies and fine frequency resolutions at low frequencies, which may present a significant advantage when analyzing highly localized speech events. Based on this motivation, this paper investigates the effectiveness of wavelet transform for feature extraction of wavelet transform for feature extraction focused on enhancing speech recognition. The proposed method is implemented using Sampled Continuous Wavelet Transform (SCWT) and its performance is tested on a speaker-independent isolated word recognizer that discerns 50 Korean words. In particular, the effect of mother wavelet employed and number of voices per octave on the performance of proposed method is investigated. Also the influence on the size of mother wavelet on the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is compared with the most prevalent conventional method, MFCC (Mel0frequency Cepstral Coefficient). The experiments show that the recognition performance of the proposed method is better than that of MFCC. But the improvement is marginal while, due to the dimensionality increase, the computational loads of proposed method is substantially greater than that of MFCC.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1033-1036
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• 2004

The study on speech recognition and understanding has been done for many years. In this paper, we propose a new type of recurrent neural network architecture for speech recognition, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units [1]. Besides that, we also proposed the new architecture and the learning algorithm of recurrent neural network such as Backpropagation Through Time (BPTT, which well-suited. The aim of the study was to observe the difference of Arabic's alphabet like "alif" until "ya". The purpose of this research is to upgrade the people's knowledge and understanding on Arabic's alphabet or word by using Recurrent Neural Network (RNN) and Backpropagation Through Time (BPTT) learning algorithm. 4 speakers (a mixture of male and female) are trained in quiet environment. Neural network is well-known as a technique that has the ability to classified nonlinear problem. Today, lots of researches have been done in applying Neural Network towards the solution of speech recognition [2] such as Arabic. The Arabic language offers a number of challenges for speech recognition [3]. Even through positive results have been obtained from the continuous study, research on minimizing the error rate is still gaining lots attention. This research utilizes Recurrent Neural Network, one of Neural Network technique to observe the difference of alphabet "alif" until "ya".

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.310-312
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• 2004

The aim of this study was to analyze the characteristics of knee joint sound in frequency domain and classify the knee joint diseases. The spectral analysis of knee joint sounds was performed using LPC(Linear Predictive Coding) and Wigner-Ville distribution. Ten normal subjects and 5 patients with meniscal tearing were enrolled. Each subject was seated on a chair and underwent active knee flexion and extension for 60 seconds. Sampling frequency was 10kHz and electronic stethoscope and electro-goniometer were applied during the knee motion for data collection. The spectral analysis showed 3 peaks in both groups and the difference energy distribution in time-frequency domain. These results suggest that the diagnosis of knee joint pathology using the auscultation could be easier and more correct.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• 음성과학
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• 제10권2호
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• pp.99-109
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• 2003

The purpose of this study was to analyze and compare head register with chest register of singers acoustically. Fifteen healthy tenor major students were participated. Fifteen healthy untrained adults were chosen as the control group for this study. Long term average (LTA) power spectrum using the Fast Fourier transform (FFT) algorithm and Linear predictive coding (LPC) filter response were made with /a/ sustained in both head (G4, 392 Hz) and chest registers (C3, 131 Hz). Statistical analysis was performed using the Mann-Whitney test. In the LTA power spectrum, head register of singers increased in the level of energy gain within the frequency of 2.2-3.4 kHz (p<0.01), and 7.5-8.4 kHz (p<0.01, p<0.05). Chest register of singers increased in the frequency of 2.2-3.1 kHz (p<0.01), 7.8-8.4 kHz (p<0.05) and around 9.6 kHz (p<0.01). The LTA power spectrum revealed a peak of acoustic energy around 2,500 Hz, known as the singer's formant and another peak of acoustic energy around 8,000 Hz in the singer's voice.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1811-1819
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• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

• 한국정보통신학회논문지
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• 제17권4호
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• pp.775-780
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• 2013

본 논문에서는 백색잡음 및 자동차잡음 환경 하에서 남녀 성별인식이 가능한 신경회로망에 의한 화자종속 음성인식 알고리즘을 제안한다. 본 논문에서 제안한 음성인식 알고리즘은 남성화자 및 여성화자를 인식하기 위하여 LPC 켑스트럼 계수를 사용하여 신경회로망에 의하여 학습된다. 본 실험에서는 백색잡음 및 자동차잡음에 대하여 총 6개의 신경회로망의 네크워크에 대한 인식결과를 나타낸다. 인식실험의 결과로부터 백색잡음에 대해서는 최대 96% 이상의 인식률, 자동차잡음에 대해서는 최대 88% 이상의 인식률을 구하였다. 마지막으로 본 실험에서는 제안하는 음성인식 알고리즘이 배경잡음 환경 하에서의 기존의 음성인식 알고리즘과 비교하여 본 방식의 알고리즘이 유효하다는 것을 실험으로 확인한다.

• 대한후두음성언어의학회지
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• 제13권1호
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• pp.52-58
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• 2002

The pitch range of the human voice is variable, extending from chest register to falsetto. Although numerous studies have investigated after laryngeal mechanism description of registers, systematic and objective studies were lack. The purpose of this study was to analyze and compare head register with chest register of singers acoustically. Fifteen healthy tenor major students were selected. Fifteen healthy untrained adults were the control group for this study. Long term average(LTA) power spectrum using the Fast Fourier transform(FFT) algorithm and Linear predictive coding (LPC) filter response were made during /a/ sustained in both head(G4, 392Hz) md chest registers (C3, 131Hz). Statistical analysis was performed using Mann-Whitney test. In the LTA power spectrum, head register of singer has increased level(energy gain) in the frequency band of 2.2-3.4kHz(p<0.01), and 7.5-8.4kHz(p<0.01, p<0.05). Chest register of singer has increased level in the frequency band of 2.2-3.1kHz(p<0.01), 7.8-8.4kHz(p<0.05) and around 9.6kHz(p<0.01). LTA power spectrum reveals a peak of acoustic energy around 2500Hz known as the singer's formant and another peak of acoustic energy around 8000Hz in singer's voice.

• 융합보안논문지
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• 제8권4호
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• pp.135-143
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• 2008

일반적인 HMI system은 원격 감시제어를 네트워크를 통하여 하고 있으나 기능이 제한 적이다. 본 논문에서는 산업용 HMI 시스템을 변형된 상태분할 알고리즘을 적용 하였다. 이 방법은, 미리 예상되는 질의어에 대한 데이터들을 갖고 모델링을 하였다. 그 결과, 모델링하는데 많은 시간이 절약되었고, 시스템을 안정적이고 정밀하게 구성하여 98.15%의 높은 인식률을 나타냈다. 음성 HMI 시스템을 산업용에 적용하여 인간이 직접적으로 활동할 수 없는 작업 환경에서도 산업용 기기들을 안정적으로 구동시킬 수 있다. HMI 시스템 엔진의 성능을 최적화하였다.