Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

Frequency-domain monaural speech enhancement has been extensively studied for over
60 years, and a great number of methods have been proposed and applied to many …

We propose TF-GridNet for speech separation. The model is a novel deep neural network
(DNN) integrating full-and sub-band modeling in the time-frequency (TF) domain. It stacks …

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

Generative AI has demonstrated impressive performance in various fields, among which
speech synthesis is an interesting direction. With the diffusion model as the most popular …

Pre-trained diffusion models have been successfully used as priors in a variety of linear
inverse problems, where the goal is to reconstruct a signal from noisy linear measurements …

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 …

This work proposes a neural network to extensively exploit spatial information for
multichannel joint speech separation, denoising and dereverberation, named SpatialNet. In …

In this work, we extend our previously proposed offline SpatialNet for long-term streaming
multichannel speech enhancement in both static and moving speaker scenarios. SpatialNet …