In many speech separation methods, the contextual information contained in the feature
sequence is mainly modeled by recurrent layer and/or self-attention mechanism. However …

Neural network-based speech separation has received a surge of interest in recent years.
Previously proposed methods either are speaker independent or extract a target speaker's …

In recent years, wsj0-2mix has become the reference dataset for single-channel speech
separation. Most deep learning-based speech separation models today are benchmarked …

Monaural speech separation is a significant research field in speech signal processing. To
achieve a better separation performance, we propose three novel joint-constraint loss …

The problems of speech separation and enhancement concern the extraction of the speech
emitted by a target speaker when placed in a scenario where multiple interfering speakers …

Deep dilated temporal convolutional networks (TCN) have been proved to be very effective
in sequence modeling. In this paper we propose several improvements of TCN for end-to …

Deep clustering is a promising technique for speech separation that is crucial to speech
communication, acoustic target detection, acoustic enhancement and speech recognition. In …

Deep learning based approaches have achieved promising performance in speaker-
dependent single-channel multispeaker speech separation. However, partly due to the label …

Speaker extraction is to extract a target speaker's voice from multi-talker speech. It simulates
humans' cocktail party effect or the selective listening ability. The prior work mostly performs …

Target speech extraction aims to extract, based on a given conditioning cue, a target speech
signal that is corrupted by interfering sources, such as noise or competing speakers …