A Separation model architecture

In Table 2, we describe the separation network architecture using a TDCN++ [21]. As compared to the original Conv-TasNet method [29], the changes to the model include the following: Instead of global layer norm, which averages statistics over frames and channels, the TDCN++ uses instance norm, also known as feature-wise global layer norm [21]. This mean-and-variance normalization is performed separately for each convolution channel across frames, with trainable scalar bias and scale parameters. The second difference is skip-residual connections from the outputs of earlier residual blocks to form the inputs of the later residual blocks. A skip-residual connection includes a transformation in the form of a dense layer with bias of the block outputs and all paths from residual connections are summed with the regular block input coming from the previous block.