• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.865-868
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• 2000

In this paper we derive a new VAD algorithm, which combines the preprocessing algorithm and the optimum decision rule. To improve the performance of the VAD algorithm we employ the speech enhancement algorithm and then apply the maximal ratio combining technique in the preprocessing procedure, which leads to maximized output SNR. Moreover, we also perform extensive computer simulations to demonstrate the performance improvement of the proposed algorithm under various background noise environments.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.167-172
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• 2002

The EVRC (Enhanced Variable Rate Codec) is currently in service as a speech cosec in digital cellular systems in North America and Korea. In the EVRC, the LSP (Line Spectral Pairs) related to energy distribution of speech signals in the frequency domain are coded by weighted split vector quantization. Considering that the LSP codebooks might be trained with the language of the develop country of the codebooks or English, it is expected that codebooks trained with Korean provide the performance improvements in the communication in Korean. In this paper, the EVRC LSP codebooks are designed with korean adopting the LBG algorithm based vector quantization, and the performance improvement of the vector quantization and the accompanying speech quality improvement are demonstrated by spectral distortion, SNR and SegSNR measurements, respectively.

• 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.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.763-772
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• 2020

During X-ray examinations in the DR system, the scattered X-rays physically generated by the patient cause image blurring in poor quality. Although X-rays to increase the contrast of images, this increases the patient's exposure dose and is likely to result in grid induced artifacts. Therefore, the purpose of this study is obtain images similar to those of real-grid with non-grid level conditions using a VGR (Virtual Grid Role) algorithm that serves as a virtual grid. Comparing MTF, SNR and CNR of non-grid and VGR algorithm images obtained with 70% exposure conditions of real-grid images showed that the MTF0.5 differed from 0.265 to 0.350 and the MTF0.1 from 0.412 to 0.467 and the SNR, CNR were also different. In addition, comparing MTF, SNR and CNR of VGR algorithm and real-grid images showed that the MTF0.5 differed from 0.350 to 0.367 and the MTF0.1 from 0.467 to 0.483 and the SNR, CNR by little.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.6
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• pp.105-114
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• 1984

A fast pseudo-noise (PN) sequence acquisition algorithm for the direct-sequence (DS) spread spectrum system is proposed. The basic concept of the algorithm has been adopted from that of the classical sliding correlator. Mathematical modeling, analysis and computer simulation of the proposed system have been done. The results of analysis and computer simulation show that the acquisition system yields a significant performance improvement over the sliding correlator. Its acquisition time takes only 45 ms when signal-to-noise ratio(SNR) is -18dB. The algorithm developed has been implemented in hardware and its experimental result is also given.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.377-382
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• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.555-560
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• 2013

This paper first proposes a classification algorithm which detects a voiced, unvoiced, and silence signal using distance measure, logarithm power and root mean square methods at each frame, then a spectrum subtraction algorithm based on a subband filter. The proposed algorithm subtracts spectrums of white noise and street noise from noisy signal based on the subband filter at each frame. In this experiment, experimental results of the proposed spectrum subtraction algorithm demonstrate using the speech and noise data of Aurora-2 database. Based on measuring the speech-to-noise ratio (SNR), experiments confirm that the proposed algorithm is effective for the speech by contaminated the noise. From the experiments, the improvement in the output SNR values was approximately 2.1 dB and 1.91 dB better for white noise and street noise, respectively.

• The Korean Journal of Nuclear Medicine Technology
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• v.28 no.1
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• pp.35-40
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• 2024

Purpose: To improve the image quality in positron emission tomography (PET), the attenuation correction technique based on the computed tomography (CT) data is important process. However, the artifact is caused by metal material during PET/CT scan, and the image quality is degraded. Therefore, the purpose of this study was to evaluate image quality according to with and without iterative metal artifact reduction (iMAR) algorithm using customized 3D printing phantom. Materials and Methods: The Hoffman and Derenzo phantoms were designed. To protect the gamma ray transmission and express the metal portion, lead substance was located to the surface. The SiPM based PET/CT was used for acquisition of PET images according to application with and without iMAR algorithm. The quantitative methods were used by signal to noise ratio (SNR), coefficient of variation (COV), and contrast to noise ratio (CNR). Results and Discussion: The results shows that the image quality applying iMAR algorithm was higher 1.15, 1.19, and 1.11 times than image quality without iMAR algorithm for SNR, COV, and CNR. Conclusion: In conclusion, the iMAR algorithm was useful for improvement of image quality by reducing the metal artifact lesion.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.293-302
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• 1989

A Dual Sensor Wiener Filter technique was used to improve the image quality of the scanning type digital radiographic system (resolution and SNR). In this method, two images were acquried simultaneously using two sensors with high and low resolution and SNR values. Using the cross Power spectrum between dual sensor outputs of the same chest radiographic image. we design a new type of Wiener Filter and implement it with fast algorithm. We compared the performance of this new dual sensor filter with conventional single sensor filters (Wiener Filter and Parametric Projec- tion Filter) . In simulation studies, it is shown that this new method has SNR improvement of 1-3 dB better than conventional filters.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.453-458
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• 2019

In this study we analyzed the tendency of the image characteristic by changing filtering factor for the proposed fast non local means (FNLM) noise reduction algorithm with designed Male Adult mesh (MASH) phantom through Geant4 application for tomographic emission (GATE) simulation program. To accomplish this purpose, MASH phantom for human copy was designed through the GATE simulation program. In addition, we acquired degraded image by adding Gaussian noise with a value of 0.005 using the MATALB program in MASH phantom. Moreover, in degraded image, the FNLM noise reduction algorithm was applied by changing the filtering factors, which set to 0.005, 0.01, 0.05, 0.1, 0.5, and 1.0 value, respectively. To quantitatively evaluate, the coefficient of variation (COV), signal to noise ratio (SNR), and contrast to noise ratio (CNR) were calculated in reconstructed images. Results of the COV, SNR and CNR were most improved in image with a filtering factor of 0.05 value. Especially, the COV was decreased with increasing filtering factor, and showed nearly constant values after 0.05 value of the filtering factor. In addition, SNR and CNR were showed that improvement with increasing filtering factor, and deterioration after 0.05 value of the filtering factor. In conclusion, we demonstrated the significance of setting the filtering factor when applying the FNLM noise reduction algorithm in degraded image.