## 6 Results
### Performance Comparison  
In this study, feature selection was based on \(p\)-values. Features with \(p\)-values below the threshold of 0.05 were considered significant to improve the model's interpretability and reduce the risk of overfitting. Tables 2-4 show the selected features from MFCC features, Hjorth features, and spectral energy features, respectively. \begin{table}
\begin{tabular}{c c} \hline
**MFCC Feature** & \(p\)**-Value** \\ \hline mfcc\_2 & \(4.198520\times 10^{-11}\) \\ \hline mfcc\_12 & \(7.750290\times 10^{-6}\) \\ \hline mfcc\_22 & \(3.081888\times 10^{-4}\) \\ \hline mfcc\_32 & \(4.038959\times 10^{-4}\) \\ \hline mfcc\_7 & \(6.701393\times 10^{-4}\) \\ \hline mfcc\_11 & \(1.539645\times 10^{-3}\) \\ \hline mfcc\_30 & \(1.756065\times 10^{-3}\) \\ \hline mfcc\_40 & \(2.829550\times 10^{-3}\) \\ \hline mfcc\_21 & \(2.907236\times 10^{-3}\) \\ \hline mfcc\_19 & \(3.459980\times 10^{-3}\) \\ \hline mfcc\_29 & \(1.106669\times 10^{-2}\) \\ \hline mfcc\_13 & \(1.471919\times 10^{-2}\) \\ \hline mfcc\_9 & \(1.511950\times 10^{-2}\) \\ \hline mfcc\_25 & \(1.820666\times 10^{-2}\) \\ \hline mfcc\_38 & \(3.290245\times 10^{-2}\) \\ \hline mfcc\_36 & \(3.836483\times 10^{-2}\) \\ \hline mfcc\_31 & \(4.856187\times 10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Selected MFCC features based on \(p\) values. \begin{table}
\begin{tabular}{c c} \hline
**Feature** & \(p\)**-Value** \\ \hline Activity & 0.001120 \\ \hline Complexity & 0.043674 \\ \hline Mobility & 0.044281 \\ \hline \end{tabular}
\end{table} 
Table 3: Selected Hjorth features based on \(p\) values. Table 5 shows the rumble identification performance when the MFCC feature extraction method was used with the ridge classifier, SVM--linear, and logistic regression as machine learning algorithms. Table 6 shows the rumble identification performance when Hjorth parameters were used as features and the decision tree classifier, AdaBoost classifier, and random forest classifier were used as machine learning algorithms. Table 7 shows the rumble identification performance when the spectral energy distribution features were used and LGBM classifier, gradient boosting classifier, and AdaBoost classifier were used as machine learning algorithms. Based on the results in Tables 5 and 6, the MFCC feature set with the ridge classifier was the best combination for identifying elephant rumbles in seismic signals. The performance of the best model for each feature extraction method was assessed using ROC curves, shown in Figures 10-12. The ridge classifier performed exceptionally well with an AUC of 0.99, indicating near-perfect classification. The decision tree classifier achieved an AUC of 0.88, while the LGBM classifier reached a respectable 0.74. Overall, the combination of MFCC features and the ridge classifier was the most effective for the classification task. \begin{table}
\begin{tabular}{c c c c} \hline
**ML Algorithm** & **Accuracy** & **BA** & **F1 Score** \\ \hline Decision tree classifier & 93.84 \(\pm\) 0.096\% & 83.33\% & 73.68\% \\ \hline AdaBoost classifier & 92.26 \(\pm\) 0.097\% & 78.33\% & 74.04\% \\ \hline Random forest classifier & 89.68 \(\pm\) 0.056\% & 70\% & 57.14\% \\ \hline \end{tabular}
\end{table} 
Table 6: Performance when Hjorth parameters were used as features. \begin{table}
\begin{tabular}{c c} \hline
**Feature** & _p_**-Value** \\ \hline SpecraEnergy\_14 & 0.007169 \\ \hline SpecraEnergy\_15 & 0.009102 \\ \hline SpecraEnergy\_13 & 0.038727 \\ \hline SpecraEnergy\_16 & 0.039069 \\ \hline \end{tabular}
\end{table} 
Table 4: Selected Spectral Energy features based on \(p\) values. \begin{table}
\begin{tabular}{c c c c} \hline
**ML Algorithm** & **Accuracy** & **BA** & **F1 Score** \\ \hline Redge classifier & 97.05 \(\pm\) 0.051\% & 95.83\% & 95.65\% \\ \hline SVM—linear & 94.04 \(\pm\) 0.067\% & 90\% & 88.89\% \\ \hline Logistic regression & 93.85 \(\pm\) 0.069\% & 84.16\% & 77.78\% \\ \hline \end{tabular}
\end{table} 
Table 5: Performance when MFCCs were used as features.