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 …

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] …

Modern speech content creation tasks such as podcasts, video voice-overs, and audio
books require studio-quality audio with full bandwidth and balanced equalization (EQ) …

The perceptual task of speech quality assessment (SQA) is a challenging task for machines
to do. Objective SQA methods that rely on the availability of the corresponding clean …

Human judgments obtained through Mean Opinion Scores (MOS) are the most reliable way
to assess the quality of speech signals. However, several recent attempts to automatically …

The goal of acoustic matching is to transform an audio recording made in one acoustic
environment to sound as if it had been recorded in a different environment, based on …

Speech intelligibility and quality assessment models are essential tools for researchers to
evaluate and improve speech processing models. However, only a few studies have …

In this work, we present a causal speech enhancement system that is designed to handle
different types of corruptions. This paper is an extended version of our contribution to the …

Automatic speech quality assessment remains challenging, as we lack complete models of
human auditory perception. Many existing full-reference models correlate well with human …