• Journal of radiological science and technology
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• v.44 no.3
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• pp.219-224
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• 2021

The purpose of these experiments is often to scan infected patients with MRI. Therefore, it is to investigate whether the antibacterial film containing silver ions, which is a non-magnetic substance, affects magnetic resonance imaging. In this experiment, the ACR phantom was used, not the patient. The ACR phantom was wrapped in an antibacterial film and the SNR, CNR, sagittal localization image, and geometrical accuracy were compared before and after. The experiment was performed 10 times and the averaged values were compared. There were no significant differences in the results of all experiments. The FDA recommends removing metal and antibacterial film masks during MRI scans. The reason is that there was one case of injury with facial burns. When I touched the antibacterial film to check the fever during the 2 hour experiment, I did not feel any particular fever. In light of the experimental results, it would be helpful to use an antibacterial film when testing an infected patient. The reason is that there isn't a difference before and after the experiment of SNR, CNR, and sagittal localization images.

• ETRI Journal
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• v.36 no.1
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• pp.167-170
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• 2014

In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.234-240
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• 2021

In this paper, the mask-based speech enhancement is improved for effective speech recognition in noise environments. In the mask-based speech enhancement, enhanced spectrum is obtained by multiplying the noisy speech spectrum by the mask. The VoiceFilter (VF) model is used as the mask estimation, and the Spectrogram Inpainting (SI) technique is used to remove residual noise of enhanced spectrum. In this paper, we propose a combined loss to further improve speech enhancement. In order to effectively remove the residual noise in the speech, the positive part of the Triplet loss is used with the component loss. For the experiment TIMIT database is re-constructed using NOISEX92 noise and background music samples with various Signal to Noise Ratio (SNR) conditions. Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI) are used as the metrics of performance evaluation. When the VF was trained with the mean squared error and the SI model was trained with the combined loss, SDR, PESQ, and STOI were improved by 0.5, 0.06, and 0.002 respectively compared to the system trained only with the mean squared error.