Generative adversarial networks (GANs) have seen remarkable progress in recent years.
They are used as generative models for all kinds of data such as text, images, audio, music …

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 …

Convolution-augmented transformer (conformer) has recently shown competitive results in
speech-domain applications, such as automatic speech recognition, continuous speech …

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED)
structure and a recurrent structure have achieved promising performance for monaural …

Since facial actions such as lip movements contain significant information about speech
content, it is not surprising that audio-visual speech enhancement methods are more …

Abstract Accurate estimation of Room Impulse Response (RIR) which captures an
environment's acoustic properties is important for speech processing and AR/VR …

This work examines the use of generative adversarial networks for reconstructing sound
fields from experimental data. It is investigated whether generative models, which learn the …

Many speech processing methods based on deep learning require an automatic and
differentiable audio metric for the loss function. The DPAM approach of Manocha et al.[1] …

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 …