## 4 Results
### Feature Extraction Results
 \\ \hline Absence & 100.0\% & 98.6\% & 100.0\% & 99.3\% & 100.0\% & 98.7\% & 100.0\% & 99.3\% \\ Alarm & 53.2\% & 69.7\% & 53.2\% & 60.4\% & 52.3\% & 79.4\% & 52.3\% & 62.3\% \\ Cat & 75.6\% & 62.6\% & 75.6\% & 68.5\% & 74.1\% & 65.3\% & 74.1\% & 72.0\% \\ Dog & 74.8\% & 69.7\% & 74.8\% & 72.1\% & 76.9\% & 79.4\% & 76.9\% & 78.1\% \\ Kitchen & 64.1\% & 71.6\% & 64.1\% & 67.7\% & 51.8\% & 48.3\% & 51.8\% & 49.2\% \\ Scream & 75.8\% & 71.9\% & 75.8\% & 73.8\% & 76.4\% & 74.1\% & 76.4\% & 74.3\% \\ Shatter & 69.1\% & 53.8\% & 69.1\% & 60.5\% & 72.5\% & 70.2\% & 72.5\% & 73.1\% \\ Shaver & 53.8\% & 69.6\% & 53.8\% & 60.7\% & 48.6\% & 43.8\% & 48.6\% & 45.6\% \\ Sam & 37.9\% & 53.0\% & 37.9\% & 44.2\% & 50.1\% & 70.6\% & 50.1\% & 57.8\% \\ Speech & 99.1\% & 97.0\% & 99.1\% & 98.0\% & 99.1\% & 86.3\% & 99.1\% & 94.0\% \\ Water & 48.7\% & 55.5\% & 48.7\% & 51.9\% & 50.2\% & 50.9\% & 50.2\% & 50.1\% \\
**Micro** & **77.6\%** & **77.6\%** & **77.6\%** & **77.6\%** & **68.4\%** & **69.7\%** & **68.4\%** & **68.7\%** \\
**Weight** & **77.6\%** & **77.3\%** & **77.6\%** & **77.3\%** & **68.4\%** & **69.7\%** & **68.4\%** & **68.7\%** \\
**Macro** & **68.4\%** & **70.3\%** & **68.4\%** & **68.8\%** & **68.4\%** & **69.7\%** & **68.4\%** & **68.7\%** \\ \hline \hline \multicolumn{10}{c}{**Log-Mel Spectrograms**} \\ \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}  \\ \hline Absence & 100.0\% & 98.4\% & 100.0\% & 99.2\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ Alarm & 62.5\% & 61.2\% & 62.5\% & 61.8\% & 70.2\% & 62.2\% & 70.2\% & 65.6\% \\ Cat & 73.4\% & 65.0\% & 73.4\% & 68.9\% & 55.9\% & 60.3\% & 55.9\% & 61.2\% \\ Dog & 52.2\% & 51.4\% & 52.2\% & 51.8\% & 49. As observed, the CWTFT scalograms have consistently achieved the highest F1-score across all categories, exceeding the performance of the MFCC features by over 10%. As mentioned earlier, this can be explained by the spectro-temporal properties of wavelets, which allows excellent time and frequency localization. The Log-Mel spectrograms gather the least F1-score out of the three features. In terms of the data imbalance, it is observed that once data is even across all categories, it improves the performance of the smaller categories. Nonetheless, the trade-off is that it reduces the F1-score for the categories with more data initially. It is also evident that performances associated with classes referring to acoustic scenes are higher than those associated to sound events. This is because sound events occur sporadically and at different instances throughout the 5-s intervals, whereas sound scenes are continuously present throughout the duration. Overall, the imbalanced dataset returns higher performance. Figure 5 accordingly shows the relevant confusion matrices for imbalanced and balanced datasets. In our previous works, we examined the response of the system performance by concatenating the cepstra from individual channels [45; 46]. This yielded a slightly better performance than using a single cepstrum after averaging the four time-aligned channels for the case of cepstral coefficients. Extracting cepstral coefficients for each channel allows a thorough consideration of all distinctive properties of the signal, which minimizes the loss of information. However, per-channel feature extraction did not cause improvement with Scalogram features, yielding a result of 90.72% as opposed to 92.33% for averaging the channels, as audio sources are already separated within its wavelet computation process. Aside from the accuracy, execution time for the inference and resource requirements is another important consideration that must be made when selecting features.