SepFormer
SepFormer
**SepFormer** is [Transformer](https://paperswithcode.com/methods/category/transformers)-based neural network for speech separation. The SepFormer learns short and long-term dependencies with a multi-scale approach that employs transformers. It is mainly composed of multi-head attention and feed-forward layers. A dual-path framework (introduced by DPRNN) is adopted and [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) are replaced with a multiscale pipeline composed of transformers that learn both short and long-term dependencies. The dual-path framework enables the mitigation of the quadratic complexity of transformers, as transformers in the dual-path framework process smaller chunks.

The model is based on the learned-domain masking approach and employs an encoder, a decoder, and a masking network, as shown in the figure. The encoder is fully convolutional, while the decoder employs two Transformers embedded inside the dual-path processing block. The decoder finally reconstructs the separated signals in the time domain by using the masks predicted by the masking network.
Attention is All You Need in Speech Separation
https://paperswithcode.com/paper/attention-is-all-you-need-in-speech
Attention is All You Need in Speech Separation