Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-
to-sequence learning. RNNs, however, are inherently sequential models that do not allow …

Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

For challenging acoustic scenarios as low signal-to-noise ratios, current speech
enhancement systems usually suffer from performance bottleneck in extracting the target …

The capability of the human to pay attention to both coarse and fine-grained regions has
been applied to computer vision tasks. Motivated by that, we propose a collaborative …

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 …

We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-
frequency (TF) domain, for monaural talker-independent speaker separation in anechoic …

We present RemixIT, a simple yet effective self-supervised method for training speech
enhancement without the need of a single isolated in-domain speech nor a noise waveform …

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

Recent advances in the design of neural network architectures, in particular those
specialized in modeling sequences, have provided significant improvements in speech …

Transformer based models have provided significant performance improvements in
monaural speech separation. However, there is still a performance gap compared to a …