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Bandwidth Extension on Raw Audio via Generative Adversarial Networks
Neural network-based methods have recently demonstrated state-of-the-art results on image synthesis and super-resolution tasks, in particular by using variants of generative adversarial networks (GANs) with supervised feature losses.
Sound Audio and Speech Processing
Adversarial Audio Super-Resolution with Unsupervised Feature Losses
Neural network-based methods have recently demonstrated state-of-the-art results on image synthesis and super-resolution tasks, in particular by using variants of generative adversarial networks (GANs) with supervised feature losses.
MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators
As a result of the increasing demand for deep neural network (DNN)-based services, efforts to develop dedicated hardware accelerators for DNNs are growing rapidly.
