We propose a novel data-driven approach to single-channel speech separation based on
deep neural networks (DNNs) to directly model the highly nonlinear relationship between …

In speech separation, both CNN-and Transformer-based models have demonstrated robust
separation capabilities, garnering significant attention within the research community …

In a multi-channel separation task with multiple speakers, we aim to recover all individual
speech signals from the mixture. In contrast to single-channel approaches, which rely on the …

In many present speech separation approaches, the separation task is formulated as a
binary classification problem. Several classification-based approaches have been proposed …

In this paper, we propose an end-to-end post-filter method with deep attention fusion
features for monaural speaker-independent speech separation. At first, a time-frequency …

Transformer has shown advanced performance in speech separation, benefiting from its
ability to capture global features. However, capturing local features and channel information …

This paper introduces a new method for multi-channel time domain speech separation in
reverberant environments. A fully-convolutional neural network structure has been used to …

Deep neural networks have shown excellent prospects in speech separation tasks.
However, obtaining good results while keeping a low model complexity remains challenging …

Speech separation is an important problem in speech processing, which targets to separate
and generate clean speech from a mixed audio containing speech from different speakers …

Time-domain Transformer neural networks have proven their superiority in speech
separation tasks. However, these models usually have a large number of network …