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 …

Recently, convolution-augmented transformer (Conformer) has achieved promising
performance in automatic speech recognition (ASR) and time-domain speech enhancement …

At the beginning of the COVID-19 pandemic, there was significant hype about the potential
impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or …

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 …

Every music composition has a composer at the core of its building block, molding it into a
style of their own. The creative compositional style of a composer varies dynamically with …

We propose an audio-to-audio neural network model that learns to denoise old music
recordings. Our model internally converts its input into a time-frequency representation by …

The goal of monaural speech enhancement is to separate clean speech from noisy speech.
Recently, many studies have employed generative adversarial networks (GAN) to deal with …

Speech enhancement concerns the processes required to remove unwanted background
sounds from the target speech to improve its quality and intelligibility. In this paper, a novel …

Music discovery services let users identify songs from short mobile recordings. These
solutions are often based on Audio Fingerprinting, and rely more specifically on the …

Recent years have seen a surge in the number of available frameworks for speech
enhancement (SE) and recognition. Whether model-based or constructed via deep learning …