An Efficient Speech Separation Network Based on Recurrent Fusion Dilated
  Convolution and Channel Attention
Junyu Wang
We present an efficient speech separation neural network, ARFDCN, which
combines dilated convolutions, multi-scale fusion (MSF), and channel attention
to overcome the limited receptive field of convolution-based networks and the
high computational cost of transformer-based networks. The suggested network
architecture is encoder-decoder based. By using dilated convolutions with
gradually increasing dilation value to learn local and global features and
fusing them at adjacent stages, the model can learn rich feature content.
Meanwhile, by adding channel attention modules to the network, the model can
extract channel weights, learn more important features, and thus improve its
expressive power and robustness. Experimental results indicate that the model
achieves a decent balance between performance and computational efficiency,
making it a promising alternative to current mainstream models for practical
applications.