• The Journal of the Acoustical Society of Korea
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• v.31 no.1
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• pp.29-38
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• 2012

This paper presents a transient noise reduction system in a speech signal. The proposed transient noise reduction system utilizes a median filter to reduce the transient noise. Since the median filter can distort speech during the noise reduction, a long-term prediction (LTP) filter is adopted as a pre-processor to minimize speech distortion. The speech information preserved by the LTP filter is re-synthesized after reducing the noise. This paper verifies the weakness of a linear prediction (LP) filter and the superiority of the LTP filter for preserving the speech component in transient noise presence environment. Applying the proposed system, the signal-to-noise ratio (SNR) of output is improved by 8dB in both speech and noise presence region, and PESQ score is increased by 1 point comparing with noisy input.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.8
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• pp.777-786
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• 2003

The phase noise characteristics of the phase-locked loop frequency synthesizer were predicted based on the analysis for phase noise contribution of noise sources. The proposed phase noise model in this paper more accurately predicts the phase noise spectrum of frequency synthesizer. To accurately model the phase noise contribution of noise sources in frequency synthesizer, the phase noise sources were analyzed via modeling of the frequency divider and phase noise components using Leeson model for reference signal source and VCO. The phase noise transfer functions to VCO from noise sources were analyzed by superposition theory and linear operation of phase-locked loop. To evaluate the phase noise prediction model, the frequency synthesizers were fabricated and were evaluated by measured data and prediction data.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.277-282
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• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.454-460
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• 2009

In this paper, we propose a residual echo and noise reduction scheme by using linear predictor for hands-free telephony applications. The proposed scheme whitens residual echo by the linear prediction during the non double-talk. But whitened residual echo signal still has speech characteristics. In this scheme, the whitened residual echo signal is more whitened by using the power of the linear prediction error signal and the linear predicted signal. After whitening process, near-end speech and ambient noise is present during double-talk but white noise will appear during non double-talk situation. By linearly predicting again the combined signal of the near-end speech and the whitened signal, the ambient noise is removed. Through computer simulation, it is shown that the proposed method performs well at the side of AIC (acoustic interference cancellation).

• 전기의세계
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• v.24 no.5
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• pp.16-20
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• 1975

초고압 송전선에 의한 전파장해는 주로 Corona 잡음에 의한 라디오 수신장해와 전파산란에 의한 TV수신장해 및 기타 무선 통신에 영향을 주는 잡음장해가 있다. 본고에서는 먼저 초고압 송전선에서의 Corona 잡음 특성을 논하고 신설 345KV 송전선 주변에서의 라디오 장해 범위를 예측해 보고자 한다.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.118-124
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• 2024

A neural network is utilized for preprocessing of de-noizing in electrocardiogram signals, retinal images, seismic waves, etc. However, the de-noizing process could provoke increase of computational time and distortion of the original signals. In this study, we investigated a neural network architecture to analyze measurement data without additional de-noizing process. From the dynamical behaviors of DNA in aqueous solution, our neural network model aimed to predict the mole fraction of each DNA in the solution. By adding white noise to the dynamics data of DNA artificially, we investigated the effect of the noise to neural network's predictions. As a result, our model was able to predict the DNA mole fraction with an error of O(0.01) when signal-to-noise ratio was O(1). This work can be applied as a efficient artificial intelligence methodology for analyzing DNA related to genetic disease or cancer cells which would be sensitive to background measuring noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.562-567
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• 2002

Noise predictive maximum likelihood(NPML) detector embeds noise predictions/ whitening process in branch metric calculation of Viterbi detector and improves the reliability of branch metric computation. Therefore, PRML detector with a noise predictor achieves some performance improvement and has an advantage of low complexity. This paper shows that NP(1221)ML system through noise predictive PR-equalized signal has less complexity and better performance than high order PR(12321)ML system in high density perpendicular magnetic recording. The simulation results are evaluated using (1) random sequence and (2) run length limited (1,7) sequence, and they are applied to linear channel and nonlinear channel with normalized linear density $1.0{\leq}K_p{\leq}3.0$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.336-342
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• 2003

Noise predictive maximum likelihood(NPML) detector embeds noise prediction/whitening process in branch metric calculation of Viterbi detector and improves the reliability of branch metric computation. Therefore, PRML detector with a noise predictor achieves some performance improvement and has an advantage of low complexity. This thesis random sequences are applied to linear channel. In perpendicular magnetic recording density KP=2.5, NP(121)ML and NP(1221)ML detection system which is based on a noise predictive PR-equalized signal are evaluated by the Performance through a computing simulation. Therefore, NPML systems are implemented and are verified by VHDL.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.377-382
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• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.