## 3 Result  
### Convolutional Neural Network Model: Testing  
In the classification stage (refer to Figure 3), we followed the process of: Flatten, Fully Connected Layers, and Softmax. The \"Flatten\" process reduces the three-dimensional matrix into a one-dimensional vector. The \"Fully Connected Layers\" process ensures that all inputs from one layer are all connected to the activation units of the next layer [39]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Basic CNN Model** & **Advanced CNN Model** \\ \hline Batch size & 32 & 32 \\ Iteration & 1262 & 1262 \\ Duration of each & 8–9 ms & 16–17 ms \\ Iteration & 50 & 50 \\ Duration of each & 10–12 s & 20–21 s \\ Learning Rate & 0.01 & 0.01 \\ Optimizer & Adam & Adam \\ \multicolumn{2}{c}{} \\ Loss function & Sparse categorical cross entropy & Sparse categorical cross entropy \\ \multicolumn{2}{c}{} \\ Hidden layer & Hidden units & Activation function & Hidden layer & Hidden units & Activation function \\  & & function & Convolutional & Retrified Linear \\  & & layer & 32 & Unit \\  & & & Convolutional & Retrified Linear \\  & & layer & 64 & Retrified Linear \\ Hidden layer & Convolutional & 128 & Retrified Linear \\ details & & & layer & 1024 \\  & & & Dense layer & 1024 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Environmental specifications for the Basic and Advanced CNN models. Figure 4: Feature learning and convolution (Adapted from [[https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584](https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584) bc134c1e2]([https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584](https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584) bc134c1e2), accessed on 5 June 2020). Figure 3: The process to train and classify the voices from Modern Family (Adapted from [[https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3) bd2b1164a53]([https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3) bd2b1164a53), accessed on 5 June 2020). Finally, the Softmax process is implemented, which extracts the predicted class of each of the ten voices. For the basic CNN model we started with a simple model that had one convolutional layer with 32 filters, (3, 3) kernel size, (1, 1) strides, ReLU activation, and \"same\" padding (see Appendix A). Flowing by a pooling layer with (2, 2) pool size. Then after the flatten layer, we had two general Neural Network layers, one had 1024 neurons with ReLU activation, and the output layer has 10 neurons with softmax activation. We used \"adam\" as the optimizer and \"sparse_categorical_crossentropy\" as the loss function [40]. Figure 5A is a plot of the loss (i.e., the gap between real data and predicted data) of training data (in blue) and the loss of validation data (in orange). Figure 5B is the chart for the accuracy of the training data (in blue) and the accuracy of the validation data (in orange). After 50 epochs training of the basic CNN model, the testing accuracy was 89.02% and was somewhat volatile (i.e., random shifts in performance). With the advanced CNN model, the model structure corresponding to the advanced CNN model can be found in Figure 6 (see source code and parameters table in the Appendix A). We started an advanced model with one convolutional layer with 32 filters, (3, 3) kernel size, (1, 1) strides, ReLU activation, and \"same\" padding, and added one batch normalization. The second convolution layer also had 32 filters, (3, 3) kernel size, (1, 1) strides, ReLU activation, and \"same\" padding and one batch normalization. Then, the network flowed by a pooling layer with (2, 2) pool size.