• Ocean Systems Engineering
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• v.1 no.3
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• pp.249-261
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• 2011

The stochastic nonlinear dynamic behavior and probability density function of ship rolling are studied using the nonlinear dynamical systems approach and probability theory. The probability density function of the rolling response is evaluated through solving the Fokker Planck Equation using the path integral method based on a Gauss-Legendre interpolation scheme. The time-dependent probability of ship rolling restricted to within the safe domain is provided and capsizing is investigated from the probability point of view. The random differential equation of ships' rolling motion is established considering the nonlinear damping, nonlinear restoring moment, white noise and colored noise wave excitation.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.991-996
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• 1994

Surrounding noise often affects the performance of speech recognition system when it is used in office or home. Especially situation is more serious when colored and nonstational noise such as an sound from television or other audio equipment is introduced. The authors proposed a voice control system for television set using an adaptive noise canceler, and it works well even is sound of television set has comparable level of speech. In this paper, a new front-end of speech recognition is introduced for the voice control system. This font-end utilizes a simplified masking model to reduce the effect of residual noise. According to experimental results, 90% correct recognition is achieved even if the level of television sound is almost 15dB higher than one of speech.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.68-75
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• 1997

Spectral subtraction is widely-used preprocessing technique for speech recognition in additive noise environments, but it requires a good estimate of the noise power spectrum. In this paper, we employ the histogram technique for the estimation of noise spectrum. This technique has advantages over other noise estimation methods in that it does not requires speech/non-speech detection and can estimate slowly-varying noise spectra. According to the speaker-independent isolated word recognition in both colored Gaussian and car noise environments under various SNR conditions. Histogram-technique-based spectral subtraction method yields superier performance to the one with conventional noise estimation method using the spectral average of initial frames during non-speech period.

• Proceedings of the IEEK Conference
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• summer
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• pp.244-248
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• 2004

This paper proposes a new noise cancellation method for speech recognition in noise environments. We determine the time adaptive threshold value using standard deviations of wavelet coefficients after wavelet transform by frames. The time adaptive threshold value is set up by using sum of standard deviations of wavelet coefficients in cA3 and weighted cD1. cA3 coefficients represent the voiced sound with lower frequency components and cD1 coefficients represent the unvoiced sound with higher frequency components. In experiments, we removed noise after adding white Gaussian noise and colored noise to original speech. The proposed method improved SNR and MSE more than wavelet transform and wavelet packet transform does. As a result of speech recognition experiment using noise speech DB, recognition performance is improved by $2\sim4\;\%.$

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.165-172
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• 2005

In speech recognition under a noisy environment, it is necessary to construct a system which reduces the noise and enhances the speech. Then it is effective to imitate the human auditory system which has an excellent analytical spectrum mechanism for speech enhancement. Accordingly, this paper proposes an adaptive method using the auditory mechanism which is called lateral inhibition. This method first estimates the noise intensity by neural network, then adaptively adjusts both the coefficients of the lateral inhibition and the adjusting coefficient of amplitude component according to the noise intensity for each input frame. It is confirmed that the proposed method is effective for speech degraded by white noise, colored noise, and road noise based on the spectral distortion measurement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.758-763
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• 2008

We introduce a noise-predictive turbo equalization using noise filter in perpendicular magnetic recording(PMR) channel. The noise filter mitigates the colored noise in high-density PMR channel. In this paper, the channel detectors used are SOVA (Soft Output Viterbi Algorithm) and BCJR algorithm which proposed by Bahl et al., and the outer decoder used is LDPC (Low Density Parity Check) code that is implemented by sum-product algorithm. Two kinds of LDPC codes are experimented. One is the 0.5Kbyte (4336,4096) LDPC code with the code rate of 0.94, and the other is 1Kbyte (8432,8192) LDPC code with the code rate of 0.97.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.293-298
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• 1993

In ground vehicles, the increasing demand for safety and ride comfort which are trade-off relation, especially at high speeds, has led to the development od actively controlled suspensions. The LQG/LTR controller can be used to design a robust feedback control system that deals with disturbance rejection properties as well as insensitivity to modelling errors and sensor noise. And when the disturbance can not be measured but is limited within a certain frequency range, a bandpass feedback to eliminate the disturbance response can be used. In this paper, hybrid controller cosisted of bandpass feedback controller and LQG/LTR controller is applied to a quarter-car model moving on a randomly profiled road. The random road profile considered as colored noise is shaped from white noise by use of shaping filter. The performance of the hybrid control system is compared with that of an LQG/LTR controlled system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.121-126
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• 2014

Demand of the data storage becomes more and more growing. This requests the next generation of storage devices to have the dominated storage capability associated with superfast read/write rate. Holographic data storage (HDS) is investigated for a long time and is considered to be a candidate for the future storage system. However, it has two-dimensional intersymbol interference that conventional one-dimensional detection solutions have not yet handled strictly because of the complexity level of system as well as the cost. We propose a new scheme that combines iterative soft output Viterbi algorithm with noise filter for improving the bit error rate performance of HDS.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.225-228
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• 2000

It is well known that a Frequency Domain Adaptive Filter (FDAF) converges faster than a Time Domain Adaptive Filter (TDAF) even when the input signal is colored such as a speech signal. We have proposed the FDAF using the Modified Discrete Fourier Transform Pair (MDFTP) and its realization and effectiveness has been confirmed through the computer simulations. In this paper, we apply the FDAF using the MDFTP to the noise cancellation system. The proposed system is based on the Adaptive Line Enhancer (ALE) and utilizes single microphone; therefore it is suitable for the portable electronic equipment. Moreover, we propose to utilize the MDFT for detecting of the pitch in the speech because the number of data points in the MDFT must be equal to the pitch to confirmed that the noise can be removed to near the level of SNR.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.703-706
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• 2002

군중잡음(crowd noise)이 발생하는 환경에서 음성 통화 및 화자 인식을 할 때에는 음성에 파열음이나 마찰음과 같은 유색잡음(colored noise)이 부가되어 원래 음성이 왜곡된다. 이와 같이 왜곡된 음성 신호를 처리할 때에는 군중잡음을 제거하는 과정이 반드시 필요하다. 본 논문에서는 전형적인 군중잡음의 모델인 쇼핑 센터 잡음을 분석하고, 그 결과를 이용하여 음성 신호처리시에 효과적으로 군중잡음만을 제거할 수 있는 모델을 제안한다. 제안된 모델은 시간 영역에서 마찰음과 파열음을 제거하고. DSI(Digital Speech Interpolation)를 이용하여 침묵 구간을 검출한다. 이때 주파수 영역에서는 이 침묵구간을 잡음으로 간주하여 이를 이용한 스펙트럼 차감법(spectral subtraction)으로 음성 신호에 부가된 군중 잡음을 제거하는 과정을 거친다.