Large language models (LLMs) have exhibited remarkable capabilities across a variety of
domains and tasks, challenging our understanding of learning and cognition. Despite the …

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 …

Existing CNN-based speech separation models face local receptive field limitations and
cannot effectively capture long time dependencies. Although LSTM and Transformer-based …

Recent multi-domain processing methods have demonstrated promising performance for
monaural speech enhancement tasks. However, few of them explain why they behave better …

Humans possess the remarkable ability to selectively attend to a single speaker amidst
competing voices and background noise, known as selective auditory attention. Recent …

We propose DiffSep, a new single channel source separation method based on score-
matching of a stochastic differential equation (SDE). We craft a tailored continuous time …

In the post-deep learning era, the Transformer architecture has demonstrated its powerful
performance across pre-trained big models and various downstream tasks. However, the …

The past decade has witnessed substantial growth of data-driven speech enhancement (SE)
techniques thanks to deep learning. While existing approaches have shown impressive …

Target speaker extraction (TSE) which has the capability to directly extract desired speech
given enrollment utterances of the target speaker has attracted more and more attention for …

Target speaker extraction (TSE) focuses on isolating the speech of a specific target speaker
from overlapped multi-talker speech, which is a typical setup in the cocktail party problem. In …