• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.37-46
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• 2001

This paper proposes a new noise suppression method using the Wavelet transform analysis. The noise suppressor using the Wavelet transform shows the more effective advantages in a babble noise than one using the short-time Fourier transform. We designed a new channel structure based on spectral subtraction of Wavelet transform coefficients and used the Wavelet mask pattern with more higher time resolution in high frequency. It showed a good adaptation capability for babble noise with a non-stationary property. To evaluate the performance of proposed noise canceller, the informal subjective listening tests (Mos tests) were performed in background noise environments (car noise, street noise, babble noise) of mobile communication. The proposed noise suppression algorithm showed about MOS 0.2 performance improvements than the suppression algorithm of EVRC in informal listening tests. The noise reduction by the proposed method was shown in spectrogram of speech signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.795-802
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from a noisy image. In order to estimate variance, information of neighboring region is used generally. The size of neighbor region is varied according to the regional characteristics of image. More accurate estimation of edge variance is due to smaller region of neighbor, on the other hands, larger region of neighbor is used to estimate the variance of flat region. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new method for determining the range of neighbors to estimate the variance in wavelet domain. Firstly, a significance map is constructed using the parent-child relationship of wavelet domain. Based on the number of the significant wavelet coefficients, the range of neighbors is determined and then the variance of the original signal is estimated using ML(maximum likelihood method. Experimental results show that the proposed method yields better results than conventional methods for image denoising.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1
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• pp.4-8
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• 1998

본 논문에서는 다양한 전화선 채널을 통하여 수집된 음성 데이터에 포함된 잡음 및 채널 왜곡을 제거하여 음성인식 시스템의 성능을 향상시키는 방법에 관하여 연구하였다. 전 화선을 통과한 음성에 포함된 채널 잡음 및 왜곡을 제거하는 방법으로는 음성신호를 보상하 는 방법으로 CMS(Cepstral Mean Subtraction), SBR(Signal Bias Removal)과 SM(Stochastic Matching)의 성능을 비교 평가하였다. 잡음제거 방식의 성능을 평가를 위하 여 음소 단위의 반연속 HMM을 이용한 화자독립 단독음 인식을 수행하였다. 인식 실험 결 과, 멜 켑스트럼을 사용한 경우에 CMS가 가장 우수한 성능을 내었고 다음으로 SM과 SBR 순으로 나타났다. 또한 특징벡터를 주변 잡음에 강인하게 하는 가중함수(RPS, BPL)를 사용 한 켑스트럼 계수와 잡음제거 방식을 함께 사용한 경우에 인식 성능이 더욱 향상되었다.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.128-137
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• 2012

When a CCD camera acquires images in the low light level environment, not only the image signals but also noise components are amplified by the AGC (auto gain control) circuit. Since the noise level in the images acquired in the dark is very high, it is difficult to remove noise with existing denoising algorithms that are targeting the images taken in the normal light condition. In this paper, we proposed an adaptive denoising algorithm that can efficiently remove significant noises caused by the low light level. First, the window including a target pixel is transformed to the frequency domain. Then the algorithm compares the characteristics of equally divided four frequency bands. Finally the noises are adaptively removed according to the frequency characteristics. The proposed algorithm successfully improves the quality of low light level images than the existing algorithms do.

• 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.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.249-252
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• 2004

본 논문에서는 TeakLite DSP 프로세서를 이용하여 캠코더에서 레코딩을 할 때 모터 소음과 주변 잡음이 입력되어 오디오 신호의 명료도가 떨어지는 문제점을 해결하기 위한 잡음 제거 기법의 실시간 구현에 대해서 기술하고자 한다. 잡음 제거를 위해서는 일반적으로 많이 사용되고 있는 Spectral Subtraction 기법을 사용하였다. 알고리즘 구현시 MIPS 감소에 효과적이었던 최적화 기법들을 적용하여 TeakLite DSP 프로세서에서 최적화되어 동작하도록 하였다. 최적화된 Spectral Subtraction 어셈블리 코드는 TeakLite DSP 프로세서에서 32 kHz, 16 bit 입력에 대해 40 MIPS에서 동작하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.432-437
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• 1998

NLMS 알고리즘을 채용한 음향반향제거기는 주변잡음에 대해서 적응필터의 계수가 오조정되어 반향제거기의 성능이 저하된다. 본 논문에서 음향반향제거기의 마이크 입력신호와 추정 오차신호의 상관도를 이용해서 주변 잡음신호에 의한 계수 오조정이 작은 적응 알고리즘과 잔여반향을 제거하기 위한 후처리기로 구성된 음향 반향 제거기를 제안한다. 기존의 NLMS 알고리즘이 입력신호의전력으로 적응상수를 정규화하지만 제안하는 알고리즘은 마이크 입력신호와 추정 오차신호의상관도와 입력신호 전력의 합으로 정규화한다. 적응필터가 반향 경로를 추정한 경우, 추정 오차신호에는 근단화자 신호가 대부분을 차지한다. 따라서 근단화자 신호가 있는 경우에는 상관도 값이 커져서 적응 상수가 작아지고 근단화자 신호에 의한 계수의 오조정을 줄일 수 있다. 후처리기도 마이크 입력신호와 추정 오차신호의 상관도를 마이크 입력신호의 전력으로 정규화한 값으로 추정 오차신호를 감쇠시킴으로써 근단화자 신호는 감쇠를 적게 하고 잔여반향을 감쇠시킨다. 멀티미디어 PC를 이용한 실험을 통해서 제안하는 알고리즘이 기존의 알고리즘에 비해서 우수한 성능을 보임을 확인했다.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.568-579
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• 2015

Recently, mobile devices with touch display panel are widely used. Accuracy and reaction speed of touch signal are very important in touch devices. Therefore, we need to develop an effective algorithm to reduce touch noise quickly and accurately. This paper proposes a touch noise reduction algorithm using Kalman filtering in consideration of signal motion. First, a specific pre-emphasis processing is applied to an input signal so as to maximize the effect of Kalman filtering. In other words, a pure signal in the touch signal increases but noise in the touch signal decreases. Next, motion of the signal is detected. Motion estimation is performed only if motion is detected. If we detect motion by using the only neighborhood of the signal, we can reduce about 75% of the computation in comparison with examining the entire area. Finally, Kalman filtering using the previous state of current signal is performed. Experimental results show that the proposed algorithm suppresses touch noise sufficiently without degradation of the pure signal

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.471-473
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• 2009

In this paper, we propose a double talk and noise robust acoustic echo canceler for hands-free telephony applications. The proposed system includes a double-talk detection method that detects the double-talk durations, which uses covariance between microphone input signa and estimated microphone input signal. And proposed adaptive algorithm for estimating acoustic echo path, uses normalized auto-covariance matrix of input signal with multiplication of residual error power and projection order of AP(affine projeciton) algorithm. It is confirmed that the proposed algorithm shows better performance from acoustic interference cancellation (AIC) viewpoint in double talk and noisy environments.

• Journal of Korea Multimedia Society
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• v.2 no.1
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• pp.99-108
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• 1999

In this paper, a new robust adaptive algorithm is proposed to improve the performance of AEC without computational burden. The proposed adaptive algorithm is based on NLMS algorithm, and its step-size is varied with the reference input signal power and the desired signal power. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. The convergence speed is comparable to NLMS algorithm at AEC application because the echo signals are attenuated about 10∼20 dBSPL. The characteristics of this algorithm is also analyzed and compared with conventional ones in this paper.