• 한국정보통신학회논문지
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• 제20권9호
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• pp.1673-1678
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• 2016

음성인식처리 분야에서 배경잡음으로 인하여 음성입력이 배경잡음으로 잘못 판단되는 원인이 되어 음성인식율의 저하를 초래한다. 이러한 종류의 잡음대책은 단순하지 않으므로 보다 고도한 잡음처리기술이 필요하게 된다. 따라서 본 논문에서는 잡음환경 중에서 정상적인 배경잡음 혹은 비정상적인 배경잡음과 지속 시간이 짧은 음성을 구별하는 알고리즘에 대하여 기술한다. 본 알고리즘은 다른 종류의 잡음과 음성을 구별하는 중요한 수단으로서 개량된 음성의 특징파리미터를 사용한다. 다음으로 다층퍼셉트론 네트워크에 의하여 잡음의 종류를 추정하는 알고리즘에 대해서 기술한다. 본 실험에서는 잡음과 음성이 구별이 가능하도록 실험적으로 확인하였다.

• 대한의용생체공학회:의공학회지
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• 제28권6호
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• pp.732-743
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• 2007

Background noise is one of the major complaints of not only hearing impaired persons but also normal listeners. This paper describes the results of two experiments in which speech recognition performance was determined for listeners with normal hearing and sensorineural hearing loss in noise environment. First, we compared speech enhancement algorithms by evaluation speech recognition ability in various speech-to-noise ratios and types of noise. Next, speech enhancement algorithms by reducing background noise were presented and evaluated to improve speech intelligibility for sensorineural hearing impairment listeners. We tested three noise reduction methods using single-microphone, such as spectrum subtraction and companding, Wiener filter method, and maximum likelihood envelop estimation. Their responses in background noise were investigated and compared with those by the speech enhancement algorithm that presented in this paper. The methods improved speech recognition test score for the sensorineural hearing impaired listeners, but not for normal listeners. The results suggest the speech enhancement algorithm with the loudness compression can improve speech intelligibility for listeners with sensorineural hearing loss.

• Current Optics and Photonics
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• 제2권2호
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• pp.165-171
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• 2018

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging method that achieves both high resolution (HR) and wide field of view. In the FPM framework, a series of low-resolution (LR) images at different illumination angles is used for high-resolution image reconstruction. On the basis of previous research, image noise can significantly degrade the FPM reconstruction result. Since the captured LR images contain a lot of dark-field images with low signal-to-noise ratio, it is very important to apply a noise-reduction process to the FPM raw dataset. However, the thresholding method commonly used for the FPM data preprocessing cannot separate signals from background noise effectively. In this work, we propose an improved thresholding method that provides a reliable background-noise threshold for noise reduction. Experimental results show that the proposed method is more efficient and robust than the conventional thresholding method.

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.162-167
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• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1114-1118
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• 2000

Impulsive sound and vibration signals in gear are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in the diagnosis of gear fault. The early detection of impulsive signal due to gear fault prevents from complete failure in gear. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the detection of impulsive signals embedded in background noise, we enhance the impulsive signals using adaptive signal processing.

• 한국도로학회논문집
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• 제18권5호
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• pp.49-56
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• 2016

PURPOSES : In this study, noise reduction effect of a two-layer porous asphalt pavement was investigated through site measurement and computer simulation. METHODS : To examine noise reduction effect, a 3 km long quiet pavement was installed by removing previous normal pavement, which had a rather low porosity. The studied site was a high-rise apartment building surrounded by the quiet pavement and Seoul ring road with heavy traffic volume, indicating relatively high background noise. RESULTS : The measurement result before and after installing the quiet pavement showed a noise reduction effect of 4.3 dB(A) at a distance of 7.5 m from the road. After validating the accuracy of simulation using SoundPLAN, the reduction in SPL(sound pressure level) at the facades by the quiet pavement was predicted by considering five different road conditions generating traffic noise from each road or in the combination of the quiet pavement and Seoul ring road. In the case of no noise from Seoul ring road, noise reduction at the facades was 4.2 dB(A) on average for 702 housing units. With background noise from Seoul ring road, however, the average SPL decreased to 2.0 dB(A). Regarding subjective response of noise, the number of housing units with a noise reduction of over 3 dB(A) was 229 out of 706 units (approximately 32%). For 77 housing units, the noise reduction was between 1~3 dB(A), while it was less than 1 dB(A) for 400 housing units. CONCLUSIONS : The overall result indicates that the quiet pavement is useful to reduce noise evenly at low and high floors compared to noise barriers, especially in the urban situation where background noise is low.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권6호
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• pp.1946-1963
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• 2014

Most background subtraction methods focus on dynamic and complex scenes without considering robustness against noise. This paper proposes a background subtraction algorithm based on dictionary learning and sparse coding for handling low light conditions. The proposed method formulates background modeling as the linear and sparse combination of atoms in the dictionary. The background subtraction is considered as the difference between sparse representations of the current frame and the background model. Assuming that the projection of the noise over the dictionary is irregular and random guarantees the adaptability of the approach in large noisy scenes. Experimental results divided in simulated large noise and realistic low light conditions show the promising robustness of the proposed approach compared with other competing methods.

• 융합신호처리학회논문지
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• 제13권1호
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• pp.44-49
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• 2012

간접접촉 심전도 측정(Indirect-Contact ECG)은 일상생활에서의 무구속 무자각 측정에 적합한 새로운 심전도 측정 방법이다. 간접접촉 심전도 측정 에 서 관측되는 큰 배경 잡음을 줄이기 위한 기초 연구로서, 본 연구에서는 간접 접촉 심전도에서 사용되는 용량성 결합 능동 전극(Capacitively coupled active electrode)의 열잡음(Thermal Noise) 모델을 구성하였다. 실험을 통해, 용량성 결합 능동 전극만의 배경 잡음의 크기가 열잡음 모델에서 예상한 수준과 거의 일치함을 확인하였다. 면으로 된 직물의 실제의 전기적 특성을 열잡음 모델에 적용하여, 면 위에서 측정된 간접접촉 심전도의 이론적 열잡음을 계산하였다. 이 연구를 통해, op-amp의 내부 잡음(intrinsic noise)은 저항에 의한 열잡음에 비해 무시할 수 있을 정도로 작음을 알 수 있었다. 그리고 열잡음의 크기와 능동 전극의 입력 저항간의 관계를 도출할 수 있게 되어, 능동 전극의 입력 저항의 최적 값 선정을 위한 향후 연구의 기반이 되었다.

• 전자공학회논문지C
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• 제34C권1호
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• pp.51-58
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• 1997

In passive sonar, the frequency spectrum of a sound radiated by underwater moving targets is composed of a broadband nonuniform background noise and narrowband discrete tonals. To detect the tonals, the background noise is estimated and removed. Using the existing algorithms that estimate the background noise, a week tonals are not detected. Because a freuqency line that is formed by tonals which are being extracted continuously is a feture of the target, we are nessesory to efficiently detect the tonals that compose the frequncy line. In this paper, we propose an efficient neural network that can remove automatically the background and detect the even errl tonals, and we extract the frequency line feature on the spectrogram by the proposed algorithm. The experimental results for a ship's radiated sound show a better performance in comparison with the existing TPM algorithm.

• 한국정보통신학회논문지
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• 제17권7호
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• pp.1571-1576
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• 2013

본 논문에서는 잡음 환경 하에서 음성신호에 대한 신호대잡음비(SNR)를 개선하기 위한 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 백색잡음 및 자동차잡음 등과 같은 배경잡음으로부터 음성신호의 SNR을 개선할 목적으로 먼저 저역, 중역, 고역 SNR 대역에서 SNR을 추정한다. 다음으로 본 알고리즘은 각 대역에서 스펙트럼을 강조함으로써 잡음으로 오염된 음성신호 속에서 잡음신호를 차감한다. 백색잡음, 자동차잡음에 의하여 오염된 음성에 대하여 본 논문에서 제안한 알고리즘이 스펙트럼 차감 방법과 비교하여 양호한 신호대잡음비 값을 구하였다. 실험결과로부터 스펙트럼 차감 방법과 비교하여 백색잡음에 대하여 최대 4.2 dB, 자동차잡음에 대하여 최대 3.7 dB의 출력 신호대잡음비가 개선된 것을 확인할 수 있었다.