## Materials and methods
### Deep Learning
Layer by layer control flow of SpectConvNet; the numbers at the corners of each rectangle represent the corresponding dimensions. RawConvNet consists of two convolution layers, both of which have 256 filters with ReLUs. The weights are randomly initialized using the methodology described by [PERSON] et al. in [39] to keep the scale of the gradients roughly the same in both layers. Each convolution layer has L2 regularization and a weight decay of 0.0001. The first convolution layer has a stride of 4, whereas the second layer has a stride of 1. A larger stride is chosen in layer 1 to lower the computation costs. The input signal to RawConvNet is a raw audio wav file downsampled to 12 kHz and normalized to have a mean of 0 and a variance of 1. The raw audio file is passed as a 20,000 \(\times\) 1 tensor to layer 1 (see Figure 8). In layer 1, the 1D tensor is convolved with 256 \(n\times 1\) filters (shown in red in layer 1) with a stride of 4 horizontally, where \(n\in\{3,10,30,80,100\}\), and activated with ReLUs. We introduced the \(n\) parameter so that we could experiment with various receptive field sizes to discover their impact \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{2}{c}{**Configuration of the Best Performing Model**} \\ \hline \multicolumn{2}{c}{**Layers**} & \multicolumn{2}{c}{**Specification**} \\ \hline \multirow{3}{*}{Layer 1} & \multirow{3}{*}{Conv-2D} & filters \(=100,\text{filterSize}=3,\text{strides}=1,\text{activation}\;=\texttt{relu}, \text{bias}=\texttt{True}\), \\  & & \(\text{biasInit}=\texttt{zeros},\text{weightsInit}=\text{uniform scaling},\text{regularizer}=\texttt{none}\), \\  & & \(\text{weightDecay}=0.001\) \\ \hline \multirow{3}{*}{Layer 2} & \multirow{3}{*}{Conv-2D} & kernelSize \(=2,\text{strides}=\texttt{none}\) \\  & & \(\text{filters}=200,\text{filterSize}=3,\text{strides}=1,\text{activation}=\texttt{relu}, \text{bias}=\texttt{True}\), \\  & & \(\text{biasInit}=\texttt{zeros},\text{weightsInit}=\text{uniform scaling},\text{regularizer}= \texttt{none}\), \\  & & \(\text{weightDecay}=0.001\) \\ \hline \multirow{3}{*}{Layer 3} & \multirow{3}{*}{Conv-2D} & filters \(=200,\text{filterSize}=3,\text{strides}=1,\text{activation}=\texttt{relu}, \text{bias}=\texttt{True}\), \\  & & \(\text{biasInit}=\texttt{zeros},\text{weightsInit}=\text{uniform scaling},\text{regularizer}= \texttt{none}\), \\  & & \(\text{weightDecay}=0.001\) \\ \hline \multirow{2}{*}{Layer 4} & \multirow{2}{*}{Maxpool-2D} & kernelSize \(=2,\text{strides}=\texttt{none}\) \\ \hline \multirow{2}{*}{Layer 5} & \multirow{2}{*}{FC} & number of units \(=50,\text{activation}\;=\texttt{relu}\) \\ \hline \multirow{2}{*}{Layer 6} & \multirow{2}{*}{Dropout} & keep probability \(=0.5\) \\ \cline{2-2}  & & FC & number of units \(=3,\text{activation}\;=\texttt{softmax}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SpectConvNet’s Layer Specification. Figure 7: RawConvNet Architecture. The numbers below each box, except for the input, are the dimensions of the resultant feature map following the corresponding operation in the box. on classification, which we discuss in detail in Section 4 below. The resultant \(5000\times 256\) feature map is shown in layer 2 in red. Batch normalization [40] is applied to the resultant signal to reduce the internal covariate shift and the signal is maxpooled with a kernel size of 4 resulting in a \(1250\times 256\) feature map. The signal is convolved using \(3\times 1\) 256 filters (shown in blue in layer 2) with a stride of 1 horizontally. The output is a \(1250\times 256\) feature map (shown in blue in layer 3). Batch normalization is applied again to the resultant signal and the signal is maxpooled with a kernel size of 4, resulting in a \(313\times 256\) feature map. In layer 4, we add a custom layer that calculates the mean of the resultant tensor of feature maps along the first axis. To put it differently, the custom layer is designed to calculate the global average over each feature map, thus reducing each feature map tensor to a single real number.