• Title/Summary/Keyword: nonstationary signal

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Signal Detection Using Wavelet Transform in Fractional Brownian Motion (Fractional Brownian Motion 잡음환경 하에서 웨이브렛 변환을 이용한 신호의 검출)

  • 김명진
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2000.08a
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    • pp.21-24
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    • 2000
  • Fractional Brownian motion(fBm)은 long-term persistence 특성을 가진 자연 현상, 1/f 잡음, 깊이가 낮은 해저에서의 배경음향잡음 등을 모델링하는데 많이 사용된다. 이 fBm은 nonstationary 유색잡음이다. 이러한 유색잡음 환경 하에서 신호를 검출하기 위한 한 방법은 Fredholm 적분방정식의 해를 구하는 것이다. 이 방정식을 이산화 하면 잡음의 공분산 행렬의 역행렬이 포함되어 계산량이 많다 본 논문에서는 fBm 잡음의 공분산 행렬을 웨이브렛 변환하여 얻어지는 행렬, 즉 fBm의 멀티스케일 성분들의 공분산행렬은 밴드화된 블록들로 근사화할 수 있다는 성질을 이용하여 적은 계산량으로 신호를 검출하는 알고리즘을 제안한다.

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Recognition for Noisy Speech by a Nonstationary AR HMM with Gain Adaptation Under Unknown Noise (잡음하에서 이득 적응을 가지는 비정상상태 자기회귀 은닉 마코프 모델에 의한 오염된 음성을 위한 인식)

  • 이기용;서창우;이주헌
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.1
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    • pp.11-18
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    • 2002
  • In this paper, a gain-adapted speech recognition method in noise is developed in the time domain. Noise is assumed to be colored. To cope with the notable nonstationary nature of speech signals such as fricative, glides, liquids, and transition region between phones, the nonstationary autoregressive (NAR) hidden Markov model (HMM) is used. The nonstationary AR process is represented by using polynomial functions with a linear combination of M known basis functions. When only noisy signals are available, the estimation problem of noise inevitably arises. By using multiple Kalman filters, the estimation of noise model and gain contour of speech is performed. Noise estimation of the proposed method can eliminate noise from noisy speech to get an enhanced speech signal. Compared to the conventional ARHMM with noise estimation, our proposed NAR-HMM with noise estimation improves the recognition performance about 2-3%.

A Study of the Compression for the Power Quality Disturbance Signal by using the Phase Estimation of Stationary Signal (정상신호의 위상 추정을 이용한 전력 품질 왜곡 신호의 압축에 관한 연구)

  • Chung, Young-Sik;Park, Chan-Woong
    • Proceedings of the KIEE Conference
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    • 2005.07a
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    • pp.341-343
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    • 2005
  • This paper introduces a compression algorithm for power quality disturbance signal via the discrete wavelet transform, DWT. Algorithm to estimate a time delay from the power quality disturbance signal is proposed. Pseudo-stationary signal is constructed from the estimated time delay. A difference signal or nonstationary signal is obtained by removing a pseudo-stationary signal from a disturbance signal. DWT is applied to a difference signal. The threshold is applied to reduce a number of coefficients. Simulation results show the resonable compression ratio while keep low signal distortion.

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Bivariate Oscillation Model for Surrogating Climate Change Scenarios in the LCRR basin

  • Lee, Taesam;Ouarda, Taha;Ahn, Yujin
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.69-69
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    • 2021
  • From the unprecedented 2011 spring flood, the residens reside by Lake Champlain and Richelieu River encountered enormous damages. The International Joint Committee (IJC) released the Lake Champlain-Richelieu River (LCRR) Plan of Study (PoS). One of the major tasks for the PoS is to investigate the possible scenarios that might happen in the LCRR basin based on the stochastic simulation of the Net Basin Supplies that calculates the amount of flow into the lake and the river. Therefore, the current study proposed a novel apporach that simulate the annual NBS teleconnecting the climate index. The proposed model employed the bivariate empirical decomposition to contamporaneously model the long-term evolution of nonstationary oscillation embeded in the annual NBS and the climate signal (here, Artic Oscillation: AO). In order to represent the variational behavior of NBS correlation structure along with the temporal revolution of the climate index, a new nonstationary parameterization concept is proposed. The results indicate that the proposed model is superior performance in preserving long and short temporal correlation. It can even preserve the hurst coefficient better than any other tested models.

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Robust Endpoint Detection and Energy Normalization (강인한 끝점 추출과 에너지 정규화)

  • 고기원;정원용
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2003.06a
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    • pp.126-129
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    • 2003
  • 자동 음성 인식(ASR) 시스템에, 끝점 추출과 에너지 정규화는 중요한 역할을 하게 된다 그러나 낮은 SNR이나 nonstationary 환경에서, 기존 방법은 끝점 추출과 에너지 정규화에 있어서 자주 실패하게 되며, ASR을 급격히 열화시키곤 한다. ASR을 수행하기 위해, 최적의 필터에 3상태 천이도를 사용하고, 필터는 정확성과 강인함을 확실히 하기 위해 여러 이론들을 이용하여 설계하였고 여러 가지 잡음이 있는 음성 신호환경에서 거의 일정한 응답을 주었다. 검출된 끝점은 곧바로 에너지 정규화에 적용된다. 실험 결과는 제안된 알고리즘이 낮은 SNR에서 에러율을 크게 감소시키고 있다는 것을 보여준다.

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Motion Detection Method For Nonstationary Video Sequences (동영상에서 물체의 움직임 검출 방법)

  • 이연철;김은이;황상원;김항준
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2000.12a
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    • pp.137-140
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    • 2000
  • 본 논문에서는 카메라의 움직임이 있는 비디오 영상에서 물체의 움직임을 검출하기 위한 방법을 제안한다. 입력 영상에서 카메라의 움직임이 있을 경우, 이를 제거하기 위해 카메라의 움직임을 추정, 보상한다. 보상된 영상에서 적응적 임계값을 사용하여 물체의 움직임을 검출한다. 제안된 방법은 카메라의 움직임이 있는 비디오 영상뿐만 아니라, 시간에 따라 조명이 변하거나 잡음이 포함된 비디오 영상에 대해 좋은 결과를 얻을 수 있다.

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Adaptive Enhancement Algorithm of Perceptual Filter Using Variable Threshold (가변 임계값을 이용한 지각 필터의 적응적인 음질 개선 알고리즘)

  • 차형태
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.6
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    • pp.446-453
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    • 2004
  • In this paper, a new adaptive perceptual filter using variable threshold to enhance audio signals degraded by additively nonstationary noise is proposed. The adaptive perceptual filter updates variable threshold each time according to the power of signal and the effect of noise variation. So the noisy audio signal is enhanced by the method which controls a residual noise effectively. The proposed algorithm uses the perceptual filter which transforms a time domain signal into frequency domain and calculates an intensity energy and an excitation energy in bark domain. In this method. the stage updated the response of filter is decided by threshold. The proposed algorithm using vairable threshold effectively controls a residual noise using the energy difference of audio signals degraded by the additive nonstationary noise. The proposed method is tested with the noisy audio signals degraded by nonstationary noise at various signal -to-noise ratios (SNR). We carry out NMR and MOS test when the input SNR is 15dB. 20dB. 25dB and 30dB. An approximate improvement of 17.4dB. 15.3dB, 12.8dB. 9.8dB in NMR and enhancement of 2.9, 2.5, 2.3, 1.7 in MOS test is achieved with the input signals. respectively.

A Selection Method of Reliable Codevectors using Noise Estimation Algorithm (잡음 추정 알고리즘을 이용한 신뢰성 있는 코드벡터 조합의 선정 방법)

  • Jung, Seungmo;Kim, Moo Young
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.7
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    • pp.119-124
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    • 2015
  • Speech enhancement has been required as a preprocessor for a noise robust speech recognition system. Codebook-based Speech Enhancement (CBSE) is highly robust in nonstationary noise environments compared with conventional noise estimation algorithms. However, its performance is severely degraded for the codevector combinations that have lower correlation with the input signal since CBSE depends on the trained codebook information. To overcome this problem, only the reliable codevector combinations are selected to be used to remove the codevector combinations that have lower correlation with input signal. The proposed method produces the improved performance compared to the conventional CBSE in terms of Log-Spectral Distortion (LSD) and Perceptual Evaluation of Speech Quality (PESQ).

Time-Varying Subspace Tracking Algorithm for Nonstationary DOA Estimation in Passive Sensor Array

  • Lim, Junseok;Song, Joonil;Pyeon, Yongkug;Sung, Koengmo
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.1E
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    • pp.7-13
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    • 2001
  • In this paper we propose a new subspace tracking algorithm based on the PASTd (Projection Approximation Subspace Tracking with deflation). The algorithm is obtained via introducing the variable forgetting factor which adapts itself to the time-varying subspace environments. The tracking capability of the proposed algorithm is demonstrated by computer simulations in an abruptly changing DOA scenario. The estimation results of the variable forgetting factor PASTd(VFF-PASTd) outperform those of the PASTd in the nonstationary case as well as in the stationary case.

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Robust Feature Extraction for Voice Activity Detection in Nonstationary Noisy Environments (음성구간검출을 위한 비정상성 잡음에 강인한 특징 추출)

  • Hong, Jungpyo;Park, Sangjun;Jeong, Sangbae;Hahn, Minsoo
    • Phonetics and Speech Sciences
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    • v.5 no.1
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    • pp.11-16
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    • 2013
  • This paper proposes robust feature extraction for accurate voice activity detection (VAD). VAD is one of the principal modules for speech signal processing such as speech codec, speech enhancement, and speech recognition. Noisy environments contain nonstationary noises causing the accuracy of the VAD to drastically decline because the fluctuation of features in the noise intervals results in increased false alarm rates. In this paper, in order to improve the VAD performance, harmonic-weighted energy is proposed. This feature extraction method focuses on voiced speech intervals and weighted harmonic-to-noise ratios to determine the amount of the harmonicity to frame energy. For performance evaluation, the receiver operating characteristic curves and equal error rate are measured.