• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6C
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• pp.572-579
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• 2010

Filtered multi-tone (FMT) is a form of multicarrier modulation utilizing frequency-domain equalization efficient in multi-path fading channels. Doppler frequency information can be employed for channel estimation and link adaptation to improve the performance. However, most previous studies have concentrated on the orthogonal frequency division multiplexing (OFDM) instead of FMT. Moreover, they have not considered the synchronization error that can commonly occur in practical systems. In this paper, we propose Doppler frequency estimation scheme that is effective in FMT systems with residual synchronization error and high noise levels.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.511-518
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• 2018

This paper deals with improving speech intelligibility by applying binary mask to time-frequency units of speech in noise. The binary mask is set to "0" or "1" according to whether speech is dominant or noise is dominant by comparing signal-to-noise ratio with pre-defined threshold. Bayesian classifier trained with Gaussian mixture model is used to estimate the binary mask of each time-frequency signal. The binary mask based noise suppressor improves speech intelligibility only in noise condition which is included in the training data. In this paper, speaker adaptation techniques for speech recognition are applied to adapt the Gaussian mixture model to a new noise environment. Experiments with noise-corrupted speech are conducted to demonstrate the improvement of speech intelligibility by employing adaption techniques in a new noise environment.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.17-25
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• 2001

Traditional adaptive noise cancelling methods rely their performance on various interfering parameters, such as convergence speed, tracking ability, numerical stability, relative frequency characteristics between target and reverberation signals, and activity of the target. In this paper, an adaptive noise cancelling method is suggested, which Provides a successful tradeoff mon these factors. It is designed to work on the transform domain, adopts the Gram-Schmidt orthogonalization process, and is implemented by the escalator algorithm. The transform domain approach supports a tradeoff between the convergence speed and numerical cost. The proposed method is verified by applying a real-data collected in the shallow waters off the east coasts of korea. It is shown that it has a good reverberation-rejection capability even for the target signal with adjacent frequency components to those of the reverberation, and its performance is invariant for the activity of the target.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.945-951
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• 2007

Noise cancellation is essential for the devices which use speech as an interface. In real environments, speech quality and recognition rates are degraded by the auditive noises coming near the microphone. In this paper, we propose a noise cancellation algorithm using stereo microphones basically. The advantage of the use of multiple microphones is that the direction information of the target source could be applied. The proposed noise canceller is based on the Wiener filter. To estimate the filter, noise and target speech frequency responses should be known and they are estimated by the spectral classification in the frequency domain. The performance of the proposed algorithm is compared with that of the well-known Frost algorithm and the generalized sidelobe canceller (GSC) with an adaptation mode controller (AMC). As performance measures, the perceptual evaluation of speech quality (PESQ), which is the most widely used among various objective speech quality methods, and speech recognition rates are adopted.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.687-693
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• 2003

In this paper, a new adaptive audio signal enhancement algorithm is proposed. In order to remove a broadband noise from a noisy signal, a filter is designed and applied adaptively to noisy audio signal. The noisy signal is first transformed to frequency domain and divided into bark domain to calculate excitation energy. A filter will be calculated to eliminate the noise by using the excitation energy and noisy energy which is obtained from a silent area. The filter is adaptively adjusted and continuously applied until the threshold point is met. The algorithm also works well even though the noise's energy change all of a sudden. SNR, NMR comparison and MOS Test are performed to show the effectiveness of the proposed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1456-1467
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• 1990

The time domain adaptive noise canceller (time domain ANC) with the adaptive compensator and its algorithm, so called compensated least mean squares(CLMS) algorithm, had been introduced to improve the performance of ANC[1]. In this paper the time domain ANC with the adaptive compensator is transformed into the frequency domain ANC with the adaptive ocmpensator. An compensated frequency-domain least mean squares(CFLMS) algorithm that can adapt the proposed frequency domain ANC is presented.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.693-698
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• 2001

Noisy data are often fitted using a smoothing parameter, controlling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this parameter minimizes the error of the result. This optimum cannot be found exactly, simply because the exact data are unknown. This paper propose the threshold value for noise reduction based on wavelet-thresholding. In the proposed method PSNR results show that the threshold value performs excellently in comparison with conventional methods without knowing the noise variance and volume of signal.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.105-106
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• 2004

음성인식, 합성 및 분석과 같은 음성신호처리 분야에 있어서 피치검출이나 포만트검출은 매우 중요하다. 주파수 영역의 스펙트럼 신호는 잡음이 부가되는 경우에도 고조파정보와 포만트 포락선 정보를 유지하기 때문에 음성신호처리분야에서 매우 유용하다고 할 수 있다. 고조파 정보나 포만트 포락선 정보는 피치검출과 포만트 주파수 검출에 직접 이용된다 하지만 두 성분을 분리하는 방법에 따라 피치검출이나 포만트 주파수 검출에 영향을 미칠 수 있으므로 기존의 방법보다 두 성분을 더 잘 분리할 수 있는 방법이 필요한 것이다. 본 논문에서는 스펙트럼 신호를 최대한 평탄화시킴으로써 포만트의 영향을 제거하고 고조파 성분을 분리해 내어 이를 피치검출에 사용한다. LSP를 이용하여 적응적 밴드에서 평탄화를 시도하고 이를 피치 검출에 이용하였다.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.431-438
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• 2001

Oxygen saturation of blood is defined as ratio of total hemoglobins density to oxyhemoglobins density And the accuracy of pulse oxymeter that measures the oxygen saturation of blood by a noninvasive method is influenced by a measuring environment, breathing and motion of patient. Especially when patient moved his arms and fingers, it is difficult to eliminate motion artifact because the motion artifact signal has features that are overlap or closed at normal signal in frequency domain. We propose the filtering method that construct the filter banks and a matched falter to improve the Problem. When experimented by the proposed method, the ratio regulation of the proposed methods has 4.1% below than an adaptive filter (39.7%) and a moving average filter (11.2%). So. the Proposed method will be able to get a stable ratio of SpO2.