• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1341-1347
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• 2000

For the DRT satellite transponder system, BER and total SNR to be required in the earth terminal are evaluated under the condition of HPA nonlinearity in the FBJ(full-band jamming) or PBJ(partial-band jamming) of uplink and downlink. In case that the satellite Inter bandwidth( Ws) is same to the earth terminal bandwidth($W_r$),in conditions of uplink JSR 10[dB], downlink JSR 10[dB] and processing gain 30[dB], linear transponder system shows that uplink SNR needs to be 14.2[dB] to achieve the total SNR 10[dB] requirement in downlink SNR 14[dB]. However, Nonlinear transponder system with OBO(output backoff) 2[dB] requires 20.1 [dB] uplink SNR. From the above results, the nonlinearity of HPA in the satellite transponder causes the degradation of BER performance so that it is of interest to consider the power increase.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.447-453
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• 2009

This paper introduces the method for detect voices and exact end point at low SNR by maximizing voice energy. Conventional VAD (Voice Activity Detection) algorithm estimates noise level so it tends to detect the end point inaccurately. Moreover, because it uses relatively long analysis range for reflecting temporal change of noise, computing load too high for application. In this paper, the SEM-VAD (Speech Energy Maximization-Voice Activity Detection) method which uses psycho-acoustical bark scale filter banks to maximize voice energy within frames is introduced. Stable threshold values are obtained at various noise environments (SNR 15 dB, 10 dB, 5 dB, 0 dB). At the test for voice detection in car noisy environment, PHR (Pause Hit Rate) was 100%accurate at every noise environment, and FAR (False Alarm Rate) shows 0% at SNR15 dB and 10 dB, 5.6% at SNR5 dB and 9.5% at SNR0 dB.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.641-648
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• 2007

The decoding structure of up-to-date ATSC DTV receivers is well optimized, and it seems that 14.6 dB is the unbreakable minimum SNR in the AWGN channel. But the SNR satisfying the Shannon capacity of DTV receivers is 11.76 dB, So, the SNR gab between the 14.6 dB and the 11.76 dB is about 2.8 dB. In order to approach the Shannon capacity we propose a recursive trellis decoder which uses reliable feedback data obtained by an RS decoder. The performance enhancement of about 0.8 dB can be achieved in case of the AWGN channel.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.169-174
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• 2010

This paper explains end-point detection method for better speech recognition rates. The proposed method determines speech and non-speech region with the entropy and the harmonic detection of speech. The end-point detection using entropy on the speech spectral energy has good performance at the high SNR(SNR 15dB) environments. At the low SNR environment(SNR 0dB), however, the threshold level of speech and noise varies, so the precise end-point detection is difficult. Therefore, this paper introduces the end-point detection methods which uses speech spectral entropy and harmonics. Experiment shows better performance than the conventional entropy methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.511-518
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• 2007

One of the major issues that has to be carefully considered when upgrading current transport network capacity, is network scalability. A novel full-meshed connected ring expansion methodology and planning tool have been proposed. A 3 to 15 node expansion ring has been studied by demonstrating a dramatic system SNR improvement when the proposed planning tool was used. The results are that node output signal and optical SNR have been improved from -16dBm/10dB to +005dBm/21dB by NPOT.

• Journal of the Korean Magnetics Society
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• v.26 no.3
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• pp.92-98
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• 2016

To aim of this study was to assess the full scan and half scan of imaging with half scan factor. Patients without a cerebral vascular disease (n = 30) and were subject to the full scan half scan, and set a region of interest in the cerebral artery from the three regions (C1, C2, C3) in the range of 7 to 8 mm. MIP (maximum intensity projection) to reconstruct the images in signal strength SNR (signal to noise ration), PSNR (peak signal noise to ratio), RMSE (root mean square error), MAE (mean absolute error) and calculated by paired t-test for use by statistics were analyzed. Scan time was half scan (4 minutes 53 seconds), the full scan (6 minutes 04 seconds). The mean measurement range (7.21 mm) of all the ROI in the brain blood vessel, was the SNR of the first C1 is completely scanned (58.66 dB), half-scan (62.10 dB), a positive correlation ($r^2=0.503$), for the second C2 SNR is completely scanned (70.30 dB), half-scan (74.67 dB) the amount of correlation ($r^2=0.575$), third C3 of a complete scan SNR (70.33 dB), half scan SNR (74.64 dB) in the amount of correlation between the It was analyzed with ($r^2=0.523$). Comparative full scan with half of SNR ($4.75{\pm}0.26dB$), PSNR ($21.87{\pm}0.28dB$), RMSE ($48.88{\pm}1.61$), was calculated as MAE ($25.56{\pm}2.2$). SNR is also applied to examine the half-scans are not many differences in the quality of the two scan methods were not statistically significant in the scan (p-value > .05) image takes less time than a full scan was used.

• Journal of Audiology & Otology
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• v.23 no.4
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• pp.181-186
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• 2019

Background and Objectives: This study compared the perception of mono-syllabic and bisyllabic words in Tamil by young normal hearing adults in the presence of multi-talker speech babble at two signal-to-noise ratios (SNRs). Further for this comparison, a speech perception in noise test was constructed using existing mono-syllabic and bi-syllabic word lists in Tamil. Subjects and Methods: A total of 30 participants with normal hearing in the age range of 18 to 25 years participated in the study. Speech-in-noise test in Tamil (SPIN-T) constructed using mono-syllabic and bi-syllabic words in Tamil was used as stimuli. The stimuli were presented in the background of multi-talker speech babble at two SNRs (0 dB and +10 dB SNR). Results: The effect of noise on SPIN-T varied with SNR. All the participants performed better at +10 dB SNR, the higher of the two SNRs considered. Additionally, at +10 dB SNR performance did not vary significantly for neither mono-syllabic or bi-syllabic words. However, a significant difference existed at 0 dB SNR. Conclusions: The current study indicated that higher SNR leads to better performance. In addition, bi-syllabic words were identified with minimal errors compared to mono-syllabic words. Spectral cues were the most affected in the presence of noise leading to more of place of articulation errors for both mono-syllabic and bi-syllabic words.

• Korean Journal of Audiology
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• v.23 no.4
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• pp.181-186
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• 2019

Background and Objectives: This study compared the perception of mono-syllabic and bisyllabic words in Tamil by young normal hearing adults in the presence of multi-talker speech babble at two signal-to-noise ratios (SNRs). Further for this comparison, a speech perception in noise test was constructed using existing mono-syllabic and bi-syllabic word lists in Tamil. Subjects and Methods: A total of 30 participants with normal hearing in the age range of 18 to 25 years participated in the study. Speech-in-noise test in Tamil (SPIN-T) constructed using mono-syllabic and bi-syllabic words in Tamil was used as stimuli. The stimuli were presented in the background of multi-talker speech babble at two SNRs (0 dB and +10 dB SNR). Results: The effect of noise on SPIN-T varied with SNR. All the participants performed better at +10 dB SNR, the higher of the two SNRs considered. Additionally, at +10 dB SNR performance did not vary significantly for neither mono-syllabic or bi-syllabic words. However, a significant difference existed at 0 dB SNR. Conclusions: The current study indicated that higher SNR leads to better performance. In addition, bi-syllabic words were identified with minimal errors compared to mono-syllabic words. Spectral cues were the most affected in the presence of noise leading to more of place of articulation errors for both mono-syllabic and bi-syllabic words.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.101-107
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• 2011

The background noises and distortions by channel are major factors that disturb the practical use of speech recognition. Usually, noise reduce the performance of speech recognition system DSR(Distributed Speech Recognition) based speech recognition also bas difficulty of improving performance for this reason. Therefore, to improve DSR-based speech recognition under noisy environment, this paper proposes a method which detects accurate speech region to extract accurate features. The proposed method distinguish speech and noise by using entropy and detection of spectral energy of speech. The speech detection by the spectral energy of speech shows good performance under relatively high SNR(SNR 15dB). But when the noise environment varies, the threshold between speech and noise also varies, and speech detection performance reduces under low SNR(SNR 0dB) environment. The proposed method uses the spectral entropy and harmonics of speech for better speech detection. Also, the performance of AFE is increased by precise speech detections. According to the result of experiment, the proposed method shows better recognition performance under noise environment.