## 4 CNN Architectures  
This section discusses about the CNN architectures used in this study to analyze noise robustness of Cochleogram and Mel Spectrogram in speaker identification and verification. The most commonly used and recent deep learning model architectures such as: basic 2 DCNN, ResNet-50, VGG-16, ECAPA-TDNN and TitaNet models are used in this study to analyze noise robustness of Cochleogram and Mel Spectrogram features in speaker identification and verification. The detail of basic 2 DCNN, VGG-16 and ResNet-50 architectures is discussed in the Tables 1-3, respectively. An ECAPA-TDNN architecture which is proposed in the study [9] is also adopted in our study, its block architecture is presented in the Figure 3. The SE-Res2 Block component of the ECAPA-TDNN is shown in the Figure 4. TitaNet architecture, which is proposed in the study [10] is also adopted in this study to evaluate the performance of the Cochleogram and Mel Spectrogram features in speaker recognition. Figure 5 presents the block diagram of TitaNet architecture. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Layer No.** & **Layer** & **Description** & **Output Size** \\ \hline
1 & input (224 \(\times\) 224 \(\times\) 3) & Cochleogram or Mel Spectrogram & \\ \hline
2 & Conv2D & f = 64, k = 3 \(\times\) 3, \(p\) = same, a = ReLu & (None, 112, 112, 64) \\ \hline
3 & BatchNormalization & - & (None, 112, 112, 64) \\ \hline
4 & Max pooling & Pool size = 2 \(\times\) 2, s = 2 \(\times\) 2 & (None, 56, 56, 64) \\ \hline
5 & Conv2D & f = 128, k = 3 \(\times\) 3, \(p\) = same, a = ReLu & (None, 56, 56, 128) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic 2 DCNN Architecture. Figure 1: Mel Spectrogram. Figure 2: Cochleogram. \begin{table}
\begin{tabular}{c c c c} \hline
**Layer No.** & **Layer** & **Description** & **Output Size** \\ \hline
1 & input (224 \(\times\) 224 \(\times\) 3) & Cochleogram or Mel Spectrogram \\ \hline  
\end{tabular}
\end{table} 
Table 2: VGG-16 Architecture. \begin{table}
\begin{tabular}{c c c c} \hline
**Layer No.** & **Layer** & **Description** & **Output Size** \\ \hline
6 & BatchNormalization & - & (None, 56, 56, 128) \\ \hline
7 & Max pooling & Pool size = 2 \(\times\) 2, s = 2 \(\times\) 2 & (None, 28, 28, 128) \\ \hline
8 & Conv2D & f = 256, k = 3 \(\times\) 3, \(p\) = same, a = ReLu & (None, 28, 28, 256) \\ \hline
9 & BatchNormalization & - & (None, 28, 28, 256) \\ \hline
10 & Max pooling & Pool size = 2 \(\times\) 2, s = 2 \(\times\) 2 & (None, 14, 14, 256) \\ \hline
11 & Conv2D & f = 512, k = 3 \(\times\) 3, \(p\) = same, a = ReLu & (None, 14, 14, 512) \\ \hline
12 & BatchNormalization & - & (None, 14, 14, 512) \\ \hline
13 & Max pooling & Pool size = 2 \(\times\) 2, s = 2 \(\times\) 2 & (None, 7, 7, 512) \\ \hline
14 & Flatten & - & (None, 25088) \\ \hline
15 & FC & f = 512, k = 3 \(\times\) 3, \(p\) = same, a = ReLu & (None, 512) \\ \hline
16 & BatchNormalization & - & (None, 512) \\ \hline
17 & Dropout & Probability = 0.5 & (None, 512) \\ \hline  & FC & & \\ \hline  & Softmax & & \\ \hline  & F = filters, k = kernel size, s = stride, \(p\) = padding, a = activation. & \\ \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer No.** & **Layer** & **Description** & **Output Size** \\ \hline
19 & Maxpool & pool = 2 \(\times\) 2, s = 2 \(\times\) 2 & (None, 7, 7, 512) \\ \hline \multicolumn{4}{c}{Flatten} \\ \hline  & Fully Connected \\ \hline  & Fully Connected \\ \hline  & Fully Connected \\ \hline  & Fully Connected \\ \hline  & Fully Connected \\ \hline  & Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._