# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 4 Results and Discussion
### Analysis of SVM  
We performed experiments to evaluate the classification performance of SVM on the custom dataset. The SVM model was trained on the training set. The model was fed with sixteen extracted features from each of the audio files of the dataset. These features per audio file are: thirteen MFCC coefficients, one spectral centroid, and one RMSE, and one ZCR. This gives an input dimension of (10,605 \(\times\) 16) for training the classifier. Validation of the model was performed in the validation set of data. We performed multiple experiments by varying the hyperparameters. This step is required to compute the best hyperplane that separates the classes. The selection process was done in a way so that the model neither underfits nor overfits. After investigating, the best setting in terms of the best results for the SVM classifier have been observed with the following specifications: kernel type of RBF; regularization parameter (C) of 1; \(\gamma\) of 0.1; and iteration of 500 times. After updating these parameters, the model was finally tested on the test set. Table 4 shows the results obtained from the SVM model. Starting with the dataset of a total of 15,147 audio files, the model was trained and validated with 70% data and tested with 30% data. The 70% percent data was split with 60% for training and 10% for validation. The accuracies obtained indicate a high percentage of classification convergence during training and validation and high accuracy performance of 97% under test, with 144 of 4545 (3.16%) misclassified samples (audio files). The recall value implies the model has correctly predicted the classes 97% of the time. Table 5 shows the results of the'scream' target compared with the other targets in precision, recall, and F1 scores. The observation of individual targets' performances shows high comparative accuracies for'scream'. The model is able to predict 98% streams accurately which is indicated by high recall value for'scream' class. To verify the result, we have employed k-fold cross-validation technique. The number of k folds has been set to 10. The combined training and validation set was divided into 10-folds, where 9-folds were used for training and 1-fold for validating. The average of k-fold scores for SVM was found to be 97%, which is consistent with our result from the previous section. The confusion matrix in Figure 6 exhibits exceptional results with only 134 misclassified samples out of 4545 samples. Twenty scream files are misclassified as glass breaking, and only one scream file is misclassified as an alarm. Since the audio files contain excessive background noise at lower SNR levels (\(-10\) dB, \(-5\) dB, 0 dB), the model is likely to confuse scream with glass breaking at these noise levels. It can also be noticed that the SVM model does not confuse scream class with background and conversation classes. The SVM shows another characteristic of the classes having edge-cases misclassifications with just another class in the dataset. It is expected for'scream' to be misclassified with 'glass'. \begin{table}
\begin{tabular}{c c} \hline \hline
**Result Summary** & \\ \hline Data samples in custom dataset & 15,147 \\ Training set:Validation set:Test set & 60\%:10\%:30\% \\ Training accuracy & 98\% \\ Validation accuracy & 97\% \\ Test accuracy & 97\% \\ Recall & 0.97 \\ Misclassified samples & 134 out of 4545 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance Metrics of SVM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline scream & 0.96 & 0.98 & 0.97 \\ glass breaking & 0.98 & 0.97 & 0.97 \\ background & 0.98 & 0.98 & 0.98 \\ alarms & 0.96 & 0.96 & 0.96 \\ conversation & 0.98 & 0.96 & 0.97 \\ accuracy & & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance Metrics of SVM. For generating the ROC curve, we have used the One-versus-Rest (OvR) approach.