## 5 Results and Discussion
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Loss** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **Recall** & **F-Measure** \\ \hline
1 & 0.738 & 0.894 & 0.892 & 0.989 & 0.9 & 0.892 & 0.896 \\
2 & 0.751 & 0.883 & 0.88 & 0.987 & 0.886 & 0.88 & 0.883 \\
3 & 0.689 & 0.894 & 0.89 & 0.989 & 0.898 & 0.89 & 0.894 \\
4 & 0.808 & 0.887 & 0.886 & 0.988 & 0.891 & 0.886 & 0.889 \\
5 & 0.654 & 0.894 & 0.891 & 0.989 & 0.899 & 0.891 & 0.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of selected models based on Bayesian optimization. Figure 2: Epochs vs. loss graphs. Table 6 and Figure 3 shows the accuracy and loss values of all the above mentioned models under the given number of training epochs. Figure 3, we concluded that the ensemble model's convergence speed is way faster than other method and its trends to convergence with fewer rounds and a lower loss value. These conclusions also demonstrate that the proposed method can guarantee a faster convergence speed, a lower loss value than other methods. Based on the stated evaluation criteria, the output of the proposed 1D CNN on the test results shown in Table 6.  
The accuracy of the top five selected classifiers based on Bayesian optimization are 89% 89%, 92%, 0.93%, 0.94%, respectively. The highest sensitivity of 93.10% and specificity of 99.49% were obtained. The specificity was more than 99% for all validation data. Overall average accuracy obtained was 94.46%, and the maximum accuracy of 95.21% for all experiments iterations. Performance measurements are presented for the weights for which the best results obtained in the study. Figure 4 shows the graphical representation of the comparison of performances for different ensembles. An ensemble model is developed by integrating five base predictors. Experimental results show that the performance of CNNs is much higher than any individual methods, as shown in Table 6 and Figure 3. Reasons behind ensemble method superiority are: (1) as the essential predictors, methods that produce satisfying results are implemented, and the variety of base predictors helps to construct models of high accuracy ensemble; (2) every base predictor is treated equally by the ensemble model, and no prior information is required. Moreover, we used a box-plot in Figure 5 to show the accuracy of the different network structures on different subsets under five-fold cross-validations. The results show that different stacked convolutional neural networks' architecture influences their recognition \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Loss** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **Recall** & **F1** \\ \hline
1 & 0.738 & 0.894 & 0.892 & 0.989 & 0.9 & 0.892 & 0.896 \\
2 & 0.32 & 0.915 & 0.901 & 0.993 & 0.93 & 0.901 & 0.915 \\
3 & 0.255 & 0.924 & 0.911 & 0.993 & 0.939 & 0.911 & 0.925 \\
4 & 0.225 & 0.934 & 0.922 & 0.994 & 0.946 & 0.922 & 0.934 \\
5 & 0.197 & 0.944 & 0.934 & 0.995 & 0.954 & 0.934 & 0.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed classification performance based on Bayesian optimization and ensemble learning. Figure 4: Comparison of Bayesian and ensemble model. accuracy. The best recognition accuracy (five-layer network) is 3.2% higher than other 1D CNN approaches. We further compared the proposed model's performance and explored how the proposed method's information fusion created the confusion matrix and AUC score. From AUC scores, it is observed that the proposed methods produced better results than other methods with scores of 0.99. Figure 6 shows the ROC curves and the calculated AUC scores. While Figure 7a shows the confusion matrix of the Bayesian optimization results, and Figure 7b of ensemble model. Values along the diagonal represent the number of correctly categorized samples for each particular class. Each confusion diagonal matrix displays the prediction accuracy of each sound class out of 10 classes.