• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1673-1678
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• 2016

In the area of the speech recognition processing, background noises are caused the incorrect response to the speech input, therefore the speech recognition rates are decreased by the background noises. Accordingly, a more high level noise processing techniques are required since these kinds of noise countermeasures are not simple. Therefore, this paper proposes an algorithm to distinguish between the stationary background noises or non-stationary background noises and the speech signal having short time duration in the noisy environments. The proposed algorithm uses the characteristic parameter of the improved speech signal as an important measure in order to distinguish different types of the background noises and the speech signals. Next, this algorithm estimates various kinds of the background noises using a multi-layer perceptron neural network. In this experiment, it was experimentally clear the estimation of the background noises and the speech signals.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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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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• v.2 no.2
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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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• v.10 no.2
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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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.

• International Journal of Highway Engineering
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• v.18 no.5
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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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• v.8 no.6
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.44-49
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• 2012

The indirect-contact ECG measurement is a newly developed method for unconstrained and nonconscious measurement in daily Life. This study is the first step to reducing the large background noise appearing in indirect-contact ECG. This study built the thermal noise model of capacitively coupled active electrode which is used in indirect-contact ECG. The results show that the level of thermal noise estimated by the thermal noise model is much the same as that of actual background noise for the capacitively coupled active electrode alone. By applying the actual electrical properties of a sample cotton cloth to the thermal noise model, the theoretical level of thermal noise in the indirect-contact ECG was estimated. The results also show that the level of op-amp's intrinsic noise is so small that it can be negligible in comparison with thermal noise of resistors. The relationship between the level of thermal noise and the resistance of the bias resistor was derived, and it is the base for the further study how to choice the optimal resistance for the bias resistor.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.1
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.