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 …

We present a causal speech enhancement model working on the raw waveform that runs in
real-time on a laptop CPU. The proposed model is based on an encoder-decoder …

Speech enhancement is a critical component of many user-oriented audio applications, yet
current systems still suffer from distorted and unnatural outputs. While generative models …

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

Removing background noise from speech audio has been the subject of considerable effort,
especially in recent years due to the rise of virtual communication and amateur recordings …

Real-world audio recordings are often degraded by factors such as noise, reverberation,
and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to …

In this work, we present CleanUNet, a causal speech denoising model on the raw waveform.
The proposed model is based on an encoder-decoder architecture combined with several …

In this work, we further develop the conformer-based metric generative adversarial network
(CMGAN) model 1 for speech enhancement (SE) in the time-frequency (TF) domain. This …

Speech enhancement plays an essential role in a wide range of speech processing
applications. Recent studies on speech enhancement tend to investigate how to effectively …

Recent literature has shown that denoising diffusion probabilistic models (DDPMs) can be
used to synthesize high-fidelity samples with a competitive (or sometimes better) quality than …