• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.07a
• /
• pp.150-151
• /
• 2010

본 논문에서는 잡음환경에서의 이중채널 음성인식을 위한 통계모델 기반 음성구간 검출 방법을 제안한다. 제안된 방법에서는 다채널 입력 신호로부터 얻어진 공간정보를 이용하여 음성 존재 및 부재 확률모델을 구하고 이를 통해 음성구간 검출을 행한다. 이때, 공간정보는 두 채널간의 상호 시간 차이와 상호 크기 차이로, 음성 존재 및 부재 확률은 가우시안 커널 밀도 기반의 확률모델로 표현된다. 그리고 음성구간은 각 시간 프레임 별 음성 존재 확률 대비 음성 부재 확률의 비를 추정하여 검출된다. 제안된 음성구간 검출 방법의 평가를 위해 검출된 구간만을 입력으로 하는 음성인식 성능을 측정한다. 실험결과, 제안된 공간정보를 이용하는 통계모델 기반의 음성구간 검출 방법이 주파수 에너지를 이용하는 통계모델 기반의 음성구간 검출 방법과 주파수 스펙트럼 밀도 기반 음성구간 검출 방법에 비해 각각 15.6%, 15.4%의 상대적 오인식률 개선을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.10a
• /
• pp.1009-1011
• /
• 2012

In this paper, a wavelength tunable light source based on mutually injected locking with two F-P LDs, has been constructed and then analyzed for wavelength shift and RIN (Relative Intensity noise) according to temperature. We have measured maximum about 2 nm for the wavelength shift and minimum -110dB/hz for the RIN. Also, the RIN and beating noise in eye patterns are increased by changing temperature high.

• Journal of IKEEE
• /
• v.3 no.2 s.5
• /
• pp.226-235
• /
• 1999

To investigate SPICE noise model and the behavior of its parameters, 1/f noise of NMOS devices fabricated by BiCMOS process is measured and compared to the various noise models and measured results. For the long channel devices, bias dependence of the drain current noise power spectral density $S_{Id}$ of NMOS is similar to the previous results. Equivalent gate noise power spectral density $S_{Vg}$ shows weak dependence on the gate and drain voltages in long channel NMOS as the previous results. However, it is shown that most of published noise models are difficult to apply to short channel devices. Therefore, in this study, with comparison of our experimental results, we have tried to find the model of 1/f noise, appropriate for our NMOS device fabricated by BiCMOS process.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.4
• /
• pp.224-229
• /
• 2005

In this paper, we Propose a novel cepstrum normalization method which normalizes the probability density function (pdf) of cepstrum for robust speech recognition in additive noise environments. While the conventional methods normalize the first- and/or second-order statistics such as the mean and/or variance of the cepstrum. the proposed method fully normalizes the statistics of cepstrum by making the pdfs of clean and noisy cepstrum identical to each other For the target Pdf, the generalized Gaussian distribution is selected to consider various densities. In recognition phase, we devise a table lookup method to save computational costs. From the speaker-independent isolated-word recognition experiments, we show that the Proposed method gives improved Performance compared with that of the conventional methods, especially in heavy noise environments.

• Journal of the Society of Naval Architects of Korea
• /
• v.29 no.2
• /
• pp.60-65
• /
• 1992

The roll response of a ship to random beam seas is investigated in terms of the threshold crossing process. The non-white excitation process is modeled as an equivalent white-noise one based on the assumption that the upcrossing properties of the response can be approximately replaced by the excitation with a white noise process with a suitable intensity. Then the non-linear damping is reinstated. The reinstated equation of motion with the equivalent white-noise intensity is solved using the equivalent non-linear method to get the desired probability density function. The proposed scheme is tested extensively with varing coefficients.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.19 no.2
• /
• pp.152-160
• /
• 2008

In this paper, we analyze the performance of a direct-conversion UHF RFID reader with the range correlation effects of the phase noise. Since a UHF RFIB system uses the same oscillator to generate the transmitted carrier and the local oscillation, the periodic interference and phase noise reduction effects occur due to time delay between two signals. Through exact theory and simulation, we verify how to cancel the periodic interference phenomena using I/Q diversity combining technique. And, we analyze phase noise reduction effects due to range correlation as a function of the tag-reader distance and the offset frequency Using these results, we simulate the symbol-error-rate performance with respect to phase noise with and without range correation effects. We show that the phase noise of the local oscillator has little effect on the symbol-error-rate performance because of phase noise reduction by range correlation.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.1
• /
• pp.1-7
• /
• 2020

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results.

• The KIPS Transactions:PartD
• /
• v.13D no.5 s.108
• /
• pp.633-640
• /
• 2006

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Density-based and grid-based clustering are two main clustering approaches. The former is famous for its capability of discovering clusters of various shapes and eliminating noises, while the latter is well known for its high speed. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set would make the clustering process extremely costly. In this paper, we propose an enhanced Density-Grid based Clustering algorithm for Large spatial database by setting a default number of intervals and removing the outliers effectively with the help of a proper measurement to identify areas of high density in the input data space. We use a density threshold DT to recognize dense cells before neighbor dense cells are combined to form clusters. When proposed algorithm is performed on large dataset, a proper granularity of each dimension in data space and a density threshold for recognizing dense areas can improve the performance of this algorithm. We combine grid-based and density-based methods together to not only increase the efficiency but also find clusters with arbitrary shape. Synthetic datasets are used for experimental evaluation which shows that proposed method has high performance and accuracy in the experiments.

• Journal of Korea Society of Industrial Information Systems
• /
• v.7 no.3
• /
• pp.9-14
• /
• 2002

In this paper we propose a method for performance enhancement of speech recognizer under noisy conditions. The parallel combination model which is presented at the PMC method using multiple Gaussian-distributed mixtures have been adapted to the variation of each mixture. The CDHMM(continuous observation density HMM) which has multiple Gaussian distributed mixtures are combined by the proposed PMC method. Also, the EM(expectation maximization) algorithm is used for adapting the model mean parameter in order to reduce the variation of the mixture density. The result of simulation, the proposed PMC adaptation method show better performance than the conventional PMC method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.4
• /
• pp.1019-1026
• /
• 1996

A modulation type classifier based on statistical moments has been successfully employed to classify PSK signals. Previously, developed Classifiers were analyzed in AWGN channel only. In this paper, a moments-based modulation type classifier to classify BPSK and QPSK signals over Rayleigh fading channel is proposed and analyzed. The moments of received signal are evaluated with the exact distribution of the received signal and a moments-based classifier is proposed. The performance evaluation of the proposed classifier in terms of the misclassification probability for BPSK and QPSK is investigated under Rayleigh fading environment.