## 4 Results  
### Feature Extraction Results  
Comparison of Cepstral, Spectral, and Spectro-Temporal Features  
Per-level and average comparisons using MFCC and Log-Mel spectrogram features against the proposed CWTFT scalograms method are seen in Table 3, which is an average of three training trials. As observed, F1-score averaging is done using three different methods: Micro, Macro, and Weighted, in order to take into account the biasing that may be caused by the data imbalance. Further, the table also entails the comparison of the system performance between imbalanced and balanced data. To achieve a balanced data, the size of the dataset is reduced to match the lowest numbered category in both training and testing sets. As per Table 2, for each category, this turns out to be 1565 files for training, based on the \"Slam\" category, and 260 files for testing, based on the \"Alarm\" category. This adds up to a total of 17,215 training files and 2860 testing files. The following results are achieved using the traditional AlexNet network, provided that this gives us the highest results as per our previous works [45; 46]. Training for the imbalanced data is achieved at 10 epochs with 1016 iterations per epoch. However, it is important to note that the number of epochs for the balanced data is 75, as it has less iterations per epoch due to the lower amount of data per category. Hence, it requires more epochs in order to reach stability. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**CWTFT Scalograms**} \\ \hline \hline \multicolumn{10}{c}{**Imbalanced Data**} \\ \hline \hline \multicolumn{10}{c}{**Lmbanced Data**} \\ \hline \hline \multicolumn{10}{c}{**Category Accuracy**} & **Precision** & **Recall** & \ \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular}  & **Accuracy** & **Precision** & **Recall** & \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular}  \\ \hline Silence & 100.0\% & 99.3\% & 100.0\% & 99.7\% & 100.0\% & 98.4\% & 100.0\% & 99.2\% \\ Alarm & 65.4\% & 63.4\% & 65.4\% & 64.4\% & 75.2\% & 83.8\% & 75.2\% & 78.7\% \\ Cat & 97.2\% & 82.3\% & 97.2\% & 89.1\% & 94.8\% & 77.9\% & 94.8\% & 86.1\% \\ Dog & 74.8\% & 74.3\% & 74.8\% & 74.5\% & 84.7\% & 89.2\% & 84.7\% & 85.8\% \\ Kitchen & 82.3\% & 82.4\% & 82.3\% & 82.4\% & 76.5\% & 59.3\% & 76.5\% & 67.2\% \\ Scream & 83.7\% & 82.4\% & 83.7\% & 83.1\% & 85.9\% & 85.2\% & 85.9\% & 86.1\% \\ Shatter & 78.2\% & 72.2\% & 78.2\% & 75.1\% & 75.4\% & 89.8\% & 75.4\% & 83.2\% \\ Shaver & 71.5\% & 83.0\% & 71.5\% & 76.8\% & 66.7\% & 75.2\% & 66.7\% & 69.5\% \\ Sam & 65.4\% & 70.6\% & 65.4\% & 67.9\% & 71.5\% & 82.1\% & 71.5\% & 77.6\% \\ Speech & 100.0\% & 97.8\% & 100.0\% & 98.9\% & 100.0\% & 92.1\% & 100.0\% & 96.7\% \\ Water & 74.2\% & 85.8\% & 74.2\% & 79.6\% & 75.2\% & 82.2\% & 75.2\% & 78.1\% \\
**Micro** & **86.0\%** & **86.0\%** & **86.0\%** & **86.0\%** & **82.4\%** & **83.2\%** & **82.4\%** & **82.6\%** \\
**Micro** & **81.2\%** & **81.2\%** & **81.2\%** & **81.0\%** & **82.4\%** & **83.2\%** & **82.4\%** & **82.6\%** \\ \hline \hline \multicolumn{10}{c}{**MFCCs**} \\ \hline \hline \multicolumn{10}{c}{**Imbalanced Data**} \\ \hline \hline \multicolumn{10}{c}{**Category Accuracy**} & **Precision** & **Recall** & \ \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular}  & **Accuracy** & **Precision** & **Recall** & \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular}