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 …

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

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays
an important role in the digital speech signal processing. According to the type of …

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 …

Score-based generative models (SGMs) have recently shown impressive results for difficult
generative tasks such as the unconditional and conditional generation of natural images …

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 …

Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a
target acoustic environment. Existing methods assume access to paired training data, where …

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 …

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 …