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Found 11 papers, 2 papers with code
Single channel speech enhancement by colored spectrograms
After denoising, the colors of spectrograms are translated to magnitudes of short-time Fourier transform (STFT) using a shallow regression neural network.
Blind Restoration of Real-World Audio by 1D Operational GANs
In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it.
Preserving the beamforming effect for spatial cue-based pseudo-binaural dereverberation of a single source
In this paper, we propose a novel approach of binaural dereverberation of a single speech source, using the differences in the interaural cues of the direct path signal and the reverberations.
Recycling an anechoic pre-trained speech separation deep neural network for binaural dereverberation of a single source
Reverberation results in reduced intelligibility for both normal and hearing-impaired listeners.
BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data
This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted during the first wave of COVID-19 (March-June 2020) in Italy.
Building an Effective Automated Assessment System for C/C++ Introductory Programming Courses in ODL Environment
In this paper, we basically identify different components that we believe are necessary in building an effective automated assessment system in the context of introductory programming courses that involve C/C++ programming.
Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation
To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver.
Integration of deep learning with expectation maximization for spatial cue based speech separation in reverberant conditions
In this paper, we formulate a blind source separation (BSS) framework, which allows integrating U-Net based deep learning source separation network with probabilistic spatial machine learning expectation maximization (EM) algorithm for separating speech in reverberant conditions.
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning
Finally, the third study shows a precision of 98. 29% on the noisy PASCAL dataset with transfer learning approach.
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96. 29%, 96. 28%, 96. 29%, 96. 28% and 96. 27% respectively.
Can AI help in screening Viral and COVID-19 pneumonia?
The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation.
