• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

• Journal of Environmental Science International
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• v.25 no.9
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• pp.1241-1251
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• 2016

A minimum threshold for the signal to noise ratio ($SNR_{min}$) has to be set in the data processing system of wind profiler radar (WPR). The data collection rate and the accuracy of the WPR wind vector depend on the $SNR_{min}$. The WPR at Uljin is operated with an $SNR_{min}$ of 1 dB which is a relatively large threshold. We found that the accuracy and the continuity of the WPR wind vector with height were directly related to the variability of the SNR and vertical gradient of the squared refractive index. We investigated a quantitative method for determining a new $SNR_{min}$ for the WPR at Uljin and it was evaluated with radiosonde data. The accuracy and continuity of the wind vector from an SNR of less than 1 dB, began to decrease at an altitude of 3.5 km. Most of the SNR values were less than -3.5 dB in altitudes higher than 3.5 km. We retrieved high-accuracy wind vectors at altitudes over 3 km where measurements were deficient with an $SNR_{min}$ of 1 dB.

• Journal of Biomedical Engineering Research
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• v.23 no.2
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• pp.109-117
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• 2002

Electrogastrography(EGG) is a noninvasive method for measuring gastric electrical activity on the abdomen resulting from gastric muscle. EGG signals have a very low frequency range (0.0083 ~0.15 Hz) and extremely low amplitude(10~100 uV). Consequently, EGG signal is easily influenced by other noises. Both finite impulse response(FIR) and infinite impulse response (IIR) filters need high orders or have phase distortions for passing very narrow bandwidth of the EGG signal. In this study, we decomposed EGG signals using a wavelet multiresolution method with Daubechies mother wavelet. The EGG signals were decomposed to seven levels. We reconstructed signal by summing the decomposed signals from level four to seven. To evaluate the performance of the wavelet multiresolution filter(WMF) with simulated EGG signal using two kinds of FIR and four kinds of IIR filters., we used two indices; signal to noise ratio(SNR) and reconstruction squared error(RSE). The SNR of WMF had 9.5, 6.9, and 4.7 dB bigger than that of the other filters at different noise levels, respectively. Also, The RSE of WMF had $1.22{\times}10^6, 1.16{\times}10^6, 1.02{\times}10^6$ smaller than that of the other filters at different noise levels, respectively. The WMF performed better in the SNR and RSE than two kinds of FIR and four kinds of IIR filters.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.385-392
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• 2017

The purpose of this study is to investigate the image quality and exposure dose according to kVp and mAs in CT and to confirm improvement in image quality according to None IR and IR(Iterative Reconstruction) levels. Measurement results of image quality using Image J, HU(Hounsfield units) and BN(Background Noise) are decreased, while SNR(Signal to Noise Ratio) and $CTDI_{vol}$(CT dose index volume) are increased as the kVp increases and there was no change of BHU(Background Hounsfield units). BN was reduced due to increased kVp, while SNR and $CTDI_{vol}$ were increased. Also, the higher IR stage, the lower BN, SI(Signal Intensity) and HU while SNR was improved by about 10~60%. Based on this, when applying IR for clinical applications, it is necessary to finely adjust kVp and mA with a phased approach.

• Journal of radiological science and technology
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• v.40 no.2
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• pp.197-203
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• 2017

The purpose of this study was to measure signal to noise ratio (SNR) according to change of equalization from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 87 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p < 0.05). In SNR results, with the quality of distributions in the order of original chest image, original chest image heart shadow and equalization chest image, equalization chest image heart shadow(p < 0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the histogram equalization chest image.

• Journal of the Korean Society of Radiology
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• v.11 no.6
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• pp.443-451
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• 2017

The purpose of this study was to comparison of measure signal to noise ratio (SNR) according to change of kernel size from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 100 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p<0.05). In SNR results, with the quality of distributions in the order of kernel size 9*9 image, kernel size 7*7 image and original chest image, kernel size 3*3 image (p<0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the kernel size chest image.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.33-38
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• 2005

Degradation is generated by several causes in the process of digitalization or transmission of data. And its essential cause is noise. Therefore, researches for wavelet-based methods which reconstruct signal degraded by noise have continued. In AWGN(addtive white gaussian noise) environment, the general trend for denoising is to use the thresholding method. Reconstructed signal includes a lot of noise because these methods only consider statistical characteristic regarding noise. In this paper, we present a new method which uses the cumulation of wavelet detail coefficients. As a result, reconstruction of edges and denoising performance are improved. Also we compare existing methods using SNR(signal-to-noise ratio) as the standard of judgement of improvemental effect.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.3
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• pp.282-291
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• 2011

Adaptive modulation(AM) is an important technique to increase the system efficiency, in which transmitter selects the most suitable modulation mode adaptively according to channel state in the temporary and spatially varying communication environment. Fixed modulation on channels with varying signal-to-noise ratio(SNR) is that the bit-errorrate(BER) probability performance is changing with the channel quality. An adaptive modulation scheme can be designed to have a BER which is constant for all channel SNRs. The correct as well as fast and simple SNR estimation is required essentially for this adaptive modulation. In order to operate adaptive modulation system effectively, in this paper, we analyze the effect of SNR estimation performance to it through the average BER and data throughput. Applying SNR estimation based on auto-correlation of decision feedback signal and others to adaptive modulation system, we also confirm performance degradation or improvement of its which is decided by SNR estimation error at each transition point of modulation level. Since SNR estimation based on auto-correlation of decision feedback signal shows stable estimation performance for various quadrature amplitude modulation(QAM) comparatively, this can be reduced degradation than others at each transition point of modulation level.

• Speech Sciences
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• v.15 no.2
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• pp.55-65
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• 2008

Recently, the multi-model based speech recognizer has been used quite successfully for noisy speech recognition. For the selection of the reference HMM (hidden Markov model) which best matches the noise type and SNR (signal to noise ratio) of the input testing speech, the estimation of the SNR value using the VAD (voice activity detection) algorithm and the classification of the noise type based on the GMM (Gaussian mixture model) have been done separately in the multi-model framework. As the SNR estimation process is vulnerable to errors, we propose an efficient method which can classify simultaneously the SNR values and noise types. The KL (Kullback-Leibler) distance between the single Gaussian distributions for the noise signal during the training and testing is utilized for the classification. The recognition experiments have been done on the Aurora 2 database showing the usefulness of the model compensation method in the multi-model based speech recognizer. We could also see that further performance improvement was achievable by combining the probability density function of the MCT (multi-condition training) with that of the reference HMM compensated by the D-JA (data-driven Jacobian adaptation) in the multi-model based speech recognizer.