# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 4 Experimental Settings
### Network Structure  
The configuration of the proposed generator is described in Table 1. We used the U-Net structure with 11 convolutional layers for the encoder \(G_{enc}\) and the decoder \(G_{dec}\) as in [22; 26]. Output shapes at each layer were represented by the number of temporal dimensions and feature maps. Conv1D in the encoder denotes a one-dimensional convolutional layer, and TrConv in the decoder means a transposed convolutional layer. We used approximately 1 s of speech (16384 samples) as the input to the encoder. The last output of the encoder was concatenated with a noise which had the shape of \(8\times 1024\) randomly sampled from the standard normal distribution \(N(0,1)\). In [27], it was reported that the generator usually learns to ignore the noise prior \(z\) in the CGAN, and we also observed a similar tendency in our experiments. For this reason, we removed the noise from the input, and the shape of the latent vector became \(8\times 1024\). The architecture of \(G_{dec}\) was a mirroring of \(G_{enc}\) with the same number and width of the filters per layer. However, skip connections from \(G_{enc}\) made the number of feature maps in every layer to be doubled. The proposed up-sampling block \(G_{up}\) consisted of 1D convolution layers, element-wise addition operations, and linear interpolation layers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Block** & **Operation** & **Output Shape** \\ \hline Input & \(16,384\times 1\) \\ \hline \multirow{8}{*}{Encoder (filterlength = 31, stride = 2)} & \(8192\times 16\) \\ \cline{2-3}  & \(4096\times 32\) \\ \cline{2-3}  & \(2048\times 32\) \\ \cline{2-3}  & \(1024\times 64\) \\ \cline{2-3}  & \(512\times 64\) \\ \cline{2-3}  & \(256\times 128\) \\ \cline{2-3}  & \(128\times 128\) \\ \cline{2-3}  & \(64\times 256\) \\ \cline{2-3}  & \(32\times 256\) \\ \cline{2-3}  & \(16\times 512\) \\ \hline Latent vector & \(8\times 1024\) \\ \hline \multirow{8}{*}{Decoder} & \(16\times 1024\) \\ \cline{2-3}  & \(32\times 512\) \\ \cline{2-3}  & \(64\times 512\) \\ \cline{2-3}  & \(128\times 256\) \\ \cline{2-3}  & \(256\times 256\) \\ \cline{2-3}  & \(512\times 128\) \\ \cline{2-3}  & \(1024\times 128\) & \(1024\times 1\) \\ \cline{2-3}  & \(2048\times 64\) & \(2048\times 1\) \\ \cline{2-3}  & \(4096\times 64\) & \(4096\times 1\) \\ \cline{2-3}  & \(8192\times 32\) & \(8192\times 1\) \\ \cline{2-3}  & \(16,384\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Architecture of the proposed generator. Output shape represented temporal dimension and feature maps. In this experiment, the proposed discriminator had the same serial convolutional layers as \(G_{enc}\). The input to the discriminator had two channels of 16,384 samples, which were the clean speech and enhanced speech. The rest of the temporal dimension and feature-maps were the same as those of \(G_{enc}\). In addition, we used LeakyReLU activation function without a normalization technique. After the last convolutional layers, there were a \(1\times 1\) convolution, and its output was fed to a fully-connected layer. To construct the proposed multi-scale discriminator, we used 5 different sub-discriminators, which were \(D_{16k}\), \(D_{8k}\), \(D_{4k}\), \(D_{2k}\), \(andD_{1k}\) trained according to in Equation (12). Each sub-discriminator had a different input dimension depending on the sampling rate. The model was trained using the Adam optimizer [37] for 80 epochs with 0.0002 learning rate for both the generator and discriminator. The batch size was 50 with 1-s audio signals that were sliced using windows of length 16,384 with 8192 overlaps. We also applied a pre-emphasis filter with impulse response \([-0.95,1]\) to all training samples. For inference, the enhanced signals were reconstructed through overlap-add. The hyper-parameters to balance the penalty terms were set as \(\lambda_{L_{1}}\) = 200 and \(\lambda_{GP}\) = 10 such that they could match the dynamic range of magnitude with respect to the generator and discriminator losses.