A convolutional network is used as the discriminator where the input consists of image, mask and guidance chan- nels, and the output is a 3-D feature of shape Rh×w×c (h, w, c representing the height, width and number of channels respectively). As shown in Figure 3, six stride...
in ConvNets each convolutional kernel attends to only a local-subset of pixels in the whole image and forces the network to focus on local patterns rather than the global context. There have been works that have focused on modeling long-range dependencies for ConvNets using image pyramids[29]...
they lack the ability to model long-range dependencies present in an image. More precisely, in ConvNets each convolutional kernel attends to only a local-subset of pixels in the whole image and forces the network to focus on local patterns rather than the global context. There...
Inspired by ResNet, residual convolution module (RCM) is introduced as the basic processing unit to ease the training of the network. As shown in the bottom-left corner of Figure 2, there is an identity mapping between the input and output of the module. In the forward propagation, input ...
It is a feed-forward neural network widely used in image recognition and computer vision tasks. CNN was initially found to be very effective in processing pixel data and has translation invariance. CNN consists of multiple convolutional layers and pooling layers, each performing specific operations. ...
It is a feed-forward neural network widely used in image recognition and computer vision tasks. CNN was initially found to be very effective in processing pixel data and has translation invariance. CNN consists of multiple convolutional layers and pooling layers, each performing specific operations. ...
Each layer includes a multi-head attention mechanism, residual and regularization network, linear feedforward network, and GRTU module. The multi-head attention mechanism is set to 8 heads. The module can focus the input multi-dimensional feature information with attention weights, and output the ...
However, the computational complexity of the network increases due to the large number of multi-head self-attention (MHSA) structures and feed-forward modules used in multiple stacked conformer blocks. In speech enhancement, people usually care most about the quality or clarity of speech. Therefore...