• The Journal of the Acoustical Society of Korea
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• v.26 no.3
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• pp.129-135
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• 2007

In this paper. we propose a prewhitening method for the km reverberation to enhance the target signal. The proposed algorithm uses the dechirping method which inversely compensates the frequency chirp rate of LFM and transforms the LFM reverberation to have stationary frequency property in each data block. Also, using the left and right adjacent beam signals as reference signals. we model frequency response of each data block by AR coefficients. From these coefficients, we implement inverse filter and prewhiten the LFM reverberation of the center beam efficiently.

• Journal of the Korean Wood Science and Technology
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• v.20 no.4
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• pp.31-42
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• 1992

최근에 서로 다른 생장추세를 나타내고 있는 남(南)애리조나산(産) 침엽수 4수종(樹種)(Pseudotsuga menziesii, Pinus ponderosa, Pinus strobiformis, Abies concolor)의 연륜폭 연대기(年代記)로 부터 반응함수를 구하기 위하여, 표준화 방법을 면밀히 검토하였으며 시계열모델에 의한 사전(事前) 여과화(濾過化)(prewhitening)로 전생장량(前生長量)을 배제하는 것이 효과적인 것인지도 조사하였다. 전통적으로 사용되던 지수(指數) 또는 질선(直線)방정식에 의한 표준화가 대부분 성공적으로 적용되었으나 최근생장이 급격히 감소된 경우는 스플라인함수를 이용하는 것이 효과적이었다. 계절(季節)모델이 적용된 Pinus ponderosa의 경우를 제외하곤 사전여과화 전후(前後)의 반응함수간(間)에 뚜렷한 차이를 발견할 수 없었다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.140-146
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• 1989

In many situations, radar targets are embedded in a clutter environment and clutter rejection is required. The clutter is unwanted radar echoes and may arise owing to reflections from ground and weather disturbances and statistical properties of the clutter vary with range and azimuth as well as time. That is, adaptive signal processing is required. In this paper, a clutter suppression algorithm based on the clutter whitening filter (WF) and doppler filter bank(DFB) is described which provides improved performance compared with conventional nonadaptive clutter suppression algorithm that is the cascaded moving target indicator (MTI) and (DFB). The clutter whitening filter algorithm is based on the Burg's maximum entropy method.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.131-137
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• 2013

In this paper we propose an improved voice activity detection (VAD) algorithm using statistical models in the signal subspace domain. A uncorrelated signal subspace is generated using embedded prewhitening technique and the statistical characteristics of the noisy speech and noise are investigated in this domain. According to the characteristics of the signals in the signal subspace, a new statistical VAD method using GGD (Generalized Gaussian Distribution) is proposed. Experimental results show that the proposed GGD-based approach outperforms the Gaussian-based signal subspace method at 0-15 dB SNR simulation conditions.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.216-222
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• 2002

Autocorrelation in time series data can affect statistical inference in correlation or regression analyses. To improve a regression model from which the residuals are autocorrelated, Yule-Walker method, nonlinear least squares estimation, maximum likelihood method and 'prewhitening' method have been used to estimate the parameters in a regression equation. This study reviewed on the estimation methods of preventing spurious correlation in the presence of autocorrelation and applied the former three methods, Yule-Walker, nonlinear least squares and maximum likelihood method, to a 20-year real data set. Monte carlo simulation was used to compare the three parameter estimation methods. However, the simulation results showed that the mean squared error distributions from the three methods simulated do not differ significantly.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.1-8
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• 1992

In the field of prosthesis arm control, the pattern classification of the EMG signal is a required basis process and also the estimation of force from collected EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why we estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation which is used in the force estimation process, the transformed signal Is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noise ratio) function is introduced.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2E
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• pp.14-20
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• 1997

The future teleconferencing systems will need an appropriate system which controls properly the acoustic echo for the convenient communication. The conventional acoustic echo cancellation algorithms involve large adaptive filters identifying the impulse response of the echo path. The use of adaptive IIR filters appears to be a reasonable way to reduce computational complexity. Effective cancellation of acoustic echo presented in teleconferencing system requires that adaptive filters have a rapid convergence speed. One of the main problems of acoustic echo cancellation techniques is that the convergence properties degrade for an highly correlated signal input such as speech signals. By the way, the introduction of linear prediction filers onto the structure of the acoustic echo cancellation represents one approach to decorrelate the speech signal. And variable step-size LMS algorithm improves the convergence speed through a little increasing of computational complexity. In this paper, we applied these two methods to the acoustic echo canceller(AEC) and showed that these methods have better performances than the conventional AEC.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.235-239
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• 1999

This thesis is concerned with the development of useful engineering techniques to detect and analyze nonlinearities in mechanical systems. The methods developed are based on the concepts of higher order spectra, in particular the bispectrum. The study of higher order statistics has been dominated by work on the bispectrum. The bispectrum can be viewed as a decomposition of the third moment(skewness) of a signal over frequency and as such is blind to symmetric nonlinearities. Initially auto higher order spectra are studied in detail with particular attention being paid to normalization method. Traditional method based on the bicoherence are studied. Under certain conditions, notably narrow band signals, the above normalization method is shown to fail and so a new technique based on prewhitening the signal in the time domain is developed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.423-430
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• 2011

In adaptive filtering, the sparseness of impulse response and input signal characteristics are very important factors of it's performance. This paper presents a subband improved proportionate normalized least square (SIPNLMS) algorithm which combines IPNLMS for impulse response sparseness and subband filtering for prewhitening the input signal. As drawing and combining the advantage of conventional approaches, the proposed algorithm, for impulse responses exhibiting high sparseness, achieve improved convergence speed and tracking ability. Simulation results, using colored signal(AR(4)) and speech input signals, show improved performance compared to fullband structure of existing methods.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.32-37
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• 2020

This paper proposes a new l₁-norm-Recursive Least Squares (RLS) algorithm which is numerically more robust than the conventional l₁-norm-RLS. The l₁-norm-RLS was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm has numerical instability in the inverse matrix calculation. In this paper, we propose a new algorithm which is robust against the numerical instability. We show that the proposed method improves stability under several numerically erroneous situations.