## 3 Result
### Model Overfitting Considerations  
Lastly, we evaluated the CNN model to test if it was overfitting. According to [PERSON] [41], overfitting is a common problem in supervised machine learning, and is present when a model fits well to observed data (i.e., training data) but not test data. In general, overfitting of a model means that our model does not generalize well from training data to unseen data [42]. There are some known causes of this phenomenon [32; 41]. For example, noise in the original dataset may be learned through the training process but then cannot be captured/modelled in the testing phase. Secondly, there is always a trade-off in the complexity of machine learning (or any predictive algorithm for that matter), whereby bias and variance are forms of prediction error in the learning process. When an algorithm has too many inputs, the consistency of accurate output tends to be less stable [41; 42]. Here we took several steps to decrease overfitting. To minimize inherent noise, we implemented a data cleaning process to remove some potential noise in our original dataset. This produced a \"cleaned\" audio signal that served in the training and testing phases. Secondly, we created a larger dataset in the training phase (i.e., \(\times\)50), which serves to provide more training for our model. Thirdly, we started with a very simple model (look at the basic model and advanced model comparison) to serve as a benchmark. Finally, we evaluated the extent to which the model performed much better on the training set than on the test set. This was not an issue as our test set resulted in an accuracy of 99%.  
Generalization testing: After using our data to train and test, we then tested the extent to which our model could be generalized to other datasets. We chose the VoxCeleb dataset (also 10 people with 30 audios for each), which has hundreds of voice recordings of celebries. We wanted to use those voice audios to test if our machine learning model performs overfitting or only works for our own data. Our process was as follows: We downloaded the audios from here: [[https://www.robots.ox.ac.uk/~vgg/data/voxceleb/](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)]([https://www.robots.ox.ac.uk/~vgg/data/voxceleb/](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)) (accessed on 24 June 2020) and we extracted 30 audios from each random person (10 in total) for 300 files total. After using our advanced machine learning model to train the data, we demonstrated that when using the advanced model, after 50 epochs of training, the testing accuracy of the CNN model is 99.95% (Figure 8A,B).  
Figure 8: Advanced CNN model applied to new data. (**A**). Prediction losses as a function of epochs. (**B**). Prediction accuracy as a function of epochs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Accuracy (\%)** \\ \hline [PERSON] & 99.60 \\ [PERSON] & 100 \\ [PERSON] & 99.79 \\ [PERSON] & 99.60 \\ [PERSON] & 99.80 \\ [PERSON] & 100 \\ [PERSON] & 99.81 \\ [PERSON] & 100 \\ [PERSON] & 100 \\ [PERSON] & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy of each class in the test set in the Advanced CNN model.  
In summary, even with the use of online audios (wav. format), our audio clean function and machine learning model continued to work well, which indicates that they are not only specific to the audios used in this study, but also for general audios unfamiliar to the model.