Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

The discrepancy between the cost function used for training a speech enhancement model
and human auditory perception usually makes the quality of enhanced speech …

Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

Adversarial loss in a conditional generative adversarial network (GAN) is not designed to
directly optimize evaluation metrics of a target task, and thus, may not always guide the …

Speech enhancement model is used to map a noisy speech to a clean speech. In the
training stage, an objective function is often adopted to optimize the model parameters …

Many deep learning-based speech enhancement algorithms are designed to minimize the
mean-square error (MSE) in some transform domain between a predicted and a target …

This letter proposes a perceptual metric for speech quality evaluation, which is suitable, as a
loss function, for training deep learning methods. This metric, derived from the perceptual …

Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both
related to a smooth transition in speech segments that may carry linguistic information, eg …

Speech enhancement systems, which denoise and dereverberate distorted signals, are
usually optimized based on signal reconstruction objectives including the maximum …

Utilizing a human-perception-related objective function to train a speech enhancement
model has become a popular topic recently. The main reason is that the conventional mean …