• 음성과학
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• 제11권2호
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• pp.227-235
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• 2004

In this paper, we proposed a new practical implementation method of the lattice transversal joint (LTJ) adaptive filter using speech codec's information. And it was applied to the adaptive echo cancellation problem to verify the efficiency of the proposed method. Realtime implementation of the LTJ adaptive filter is very difficult due to high computational complexity for the filter coefficients compensation. However, in case of using speech codec, complexity can be reduced since linear predictive coding (LPC) coefficients are updated each frame or sub-frame instead of every sample. Furthermore, LPC coefficients can be acquired from speech decoder and transformed to the reflection coefficients. Therefore, the computational complexity for updates of the reflection coefficients can be reduced. The effectiveness of the proposed LTJ adaptive filter was verified by the experiments about convergence and tracking performance of the adaptive echo canceller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.33-36
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• 1988

In this paper, the starting point of affricate is detected from the first predictor coefficient of a 12-pole linear predictive coding (LPC) analysis and phonemic segmentation is done through measuring short time energy and zero crossing rate. By this segmentation method, the duration of an aspirate can be mearsured in order to detect an aspirate or not.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.711-714
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• 2009

잡음환경 하의 회화에서 잡음량을 줄이고 신호처리 시스템의 성능을 향상시키기 위해서는 잡음량에 따라서 적응적으로 처리되는 신호처리 시스템이 필요하다. 또한 잡음이 중첩된 음성으로부터 잡음을 제거하기 위해서는 잡음의 크기에 따라서 음성 처리 시스템의 파라미터를 변경하는 것이 양호한 음질의 음성을 재생하는데 바람직하다. 따라서 본 논문에서는 음성 속에 포함되는 잡음량을 인식하는 방법으로 선형예측계수를 구하여 시간지연신경회로망(Time-delay neural network: TDNN)의 입력으로 사용하여 학습시키는 잡음량을 인식하는 방법을 제안한다. 본 잡음량 인식은 다양한 배경잡음에 의하여 열화된 3종류의 음성이 TDNN에 의하여 학습되어진다. 본 실험에서는 Aurora2 데이터베이스를 사용하여 여러 잡음에 대하여 양호한 인식결과를 확인할 수 있었다.

• Bulletin of the Korean Chemical Society
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• 제28권9호
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• Korean Journal of Radiology
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• 제19권6호
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• 한국음향학회지
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• 제21권3호
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• pp.304-309
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• 2002

LSP (Line Spectrum Pairs) 파라미터는 음성코덱 (codec)이나 인식기에서 음성신호를 분석하여 전송형이나 저장형 파라미터로 변환되어, 주로 저전송률 음성부호화기에 사용된다. 그러나 LPC (Linear Predictive Coding) 계수를 LSP로 변환하는 방법이 복잡하여 계산시간이 많이 소요된다는 단점이 있다. 기존의 LSP변환 방법 중 음성 부호화기에서 주로 사용하는 실근 (real root)방법은 근을 구하기 위해 주파수 영역을 순차적으로 검색하기 때문에 계산시간이 많이 소요되는 단점을 갖는다. 본 논문에서 기존의 실근 방법과 비교 평가한 알고리즘은 첫 번째 검색 대역에 멜 스케일 (met scale)을 사용하였고, 두 번째는 LSP 파라미터의 분포 특성을 조사하여 이를 토대로 검색구간의 순서와 검색간격을 달리 하였다. 실험결과, 기존의 실근 방식에 비하여 두 가지 방식 모두가 변환시간의 47% 이상이 감소되는데 반하여 동일한 근을 찾음을 알 수가 있었다.

• Nutrition Research and Practice
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• 제15권1호
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• pp.95-105
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• 2021

BACKGROUND/OBJECTIVES: The measurement of body composition, including muscle and fat mass, remains challenging in large epidemiological studies due to time constraint and cost when using accurate modalities. Therefore, this study aimed to develop and validate prediction equations according to sex to measure lean body mass (LBM), appendicular skeletal muscle mass (ASM), and body fat mass (BFM) using anthropometric measurement, serum creatinine level, and lifestyle factors as independent variables and dual-energy X-ray absorptiometry as the reference method. SUBJECTS/METHODS: A sample of the Korean general adult population (men: 7,599; women: 10,009) from the Korean National Health and Nutrition Examination Survey 2008-2011 was included in this study. The participants were divided into the derivation and validation groups via a random number generator (with a ratio of 70:30). The prediction equations were developed using a series of multivariable linear regressions and validated using the Bland-Altman plot and intraclass correlation coefficient (ICC). RESULTS: The initial and practical equations that included age, height, weight, and waist circumference had a different predictive ability for LBM (men: R2 = 0.85, standard error of estimate [SEE] = 2.7 kg; women: R2 = 0.78, SEE = 2.2 kg), ASM (men: R2 = 0.81, SEE = 1.6 kg; women: R2 = 0.71, SEE = 1.2 kg), and BFM (men: R2 = 0.74, SEE = 2.7 kg; women: R2 = 0.83, SEE = 2.2 kg) according to sex. Compared with the first prediction equation, the addition of other factors, including serum creatinine level, physical activity, smoking status, and alcohol use, resulted in an R2 that is higher by 0.01 and SEE that is lower by 0.1. CONCLUSIONS: All equations had low bias, moderate agreement based on the Bland-Altman plot, and high ICC, and this result showed that these equations can be further applied to other epidemiologic studies.

• 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.

• 한국음향학회지
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• 제20권7호
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• pp.69-75
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• 2001

화자확인에서 화자내 변이, 잡음환경, 그리고 학습환경과 인식 환경의 불일치는 화자확인 시스템이 실용화될 수 없는 가장 큰 원인이다. 본 연구에서는, 실제 환경에 강인한 화자 확인 시스템의 구현에 초점을 맞추어 음성 전처리 과정인 잡음환경에 강인한 끝점추출 알고리즘, 잡음제거 및 마이크특성 보상기법, LPG(Linear Predictive Coefficient)켑스트럼 가중치에 의한 화자간 변별력 향상 기법을 제안한다. 실험 결과, LPC잔차신호(residue)를 이용한 끝점추출 알고리즘을 사용한 경우 약 17.65% 가량의 끝점 추출 에러율을 향상시켰으며, 제안한 잡음제거 및 마이크특성 보상기법을 사용한 경우 다른 마이크 환경에서 화자 오인식율이 약 36.93% 가량 개선되었다. 또한, 제안한 LPC켑스트럼 가중치에 의한 화자간 변별력 향상 기법은 평균 화자 오인식율을 약 6.515% 향상시켰다.

• 한국보건간호학회지
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• 제33권3호
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• pp.354-363
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• 2019

Purpose: This study was undertaken to identify factors related to quit-smoking intention based on the planned behavior theory among out-of-school youths. Methods: This study was a quantitative research, data were collected during Nov. 1. 2018 to Feb. 28, 2019, study subjects were 189 out-of-school youths in Youth support Center located in A, B, C area city. The data were analyzed using independent sample t-test and one-way ANOVA, Pearson's correlation coefficient, and multiple linear regression. Results: There was a significant differences of quit smoking intention according to age, weekly allowance, participate of antismoking program. There were positive correlations between quit smoking intention and attitude to non-smoking and perceived behavior control. In smoking cessation intention influenced by predictive variable, age, weekly allowance, attitude to non-smoking, and perceived behavior control explain 26.8% smoking cessation intention. Conclusion: We believe that findings from this study will help to develop the specific smoking cessation education program for out-of-school youth's health behaviors.