Supervised speech separation based on deep learning: An overview
Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …
Traditionally, speech separation is studied as a signal processing problem. A more recent …
Recent advances of deep learning in bioinformatics and computational biology
Extracting inherent valuable knowledge from omics big data remains as a daunting problem
in bioinformatics and computational biology. Deep learning, as an emerging branch from …
in bioinformatics and computational biology. Deep learning, as an emerging branch from …
Machine learning of molecular electronic properties in chemical compound space
The combination of modern scientific computing with electronic structure theory can lead to
an unprecedented amount of data amenable to intelligent data analysis for the identification …
an unprecedented amount of data amenable to intelligent data analysis for the identification …
Model learning for robot control: a survey
D Nguyen-Tuong, J Peters - Cognitive processing, 2011 - Springer
Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …
dynamics models of the robot's own body and controllable external objects. It is widely …
A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes
Early diagnosis remains a significant challenge for many neurological disorders, especially
for rare disorders where studying large cohorts is not possible. A novel solution that …
for rare disorders where studying large cohorts is not possible. A novel solution that …
Classifying radio galaxies with the convolutional neural network
We present the application of a deep machine learning technique to classify radio images of
extended sources on a morphological basis using convolutional neural networks (CNN). In …
extended sources on a morphological basis using convolutional neural networks (CNN). In …
Application of continuous wavelet transform and convolutional neural network in decoding motor imagery brain-computer interface
HK Lee, YS Choi - Entropy, 2019 - mdpi.com
The motor imagery-based brain-computer interface (BCI) using electroencephalography
(EEG) has been receiving attention from neural engineering researchers and is being …
(EEG) has been receiving attention from neural engineering researchers and is being …
Deep neural networks to enable real-time multimessenger astrophysics
Gravitational wave astronomy has set in motion a scientific revolution. To further enhance
the science reach of this emergent field of research, there is a pressing need to increase the …
the science reach of this emergent field of research, there is a pressing need to increase the …
Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks
Rapid and reliable identification of insects is important in many contexts, from the detection
of disease vectors and invasive species to the sorting of material from biodiversity …
of disease vectors and invasive species to the sorting of material from biodiversity …
The sparseness of mixed selectivity neurons controls the generalization–discrimination trade-off
Intelligent behavior requires integrating several sources of information in a meaningful
fashion—be it context with stimulus or shape with color and size. This requires the …
fashion—be it context with stimulus or shape with color and size. This requires the …