## 4 Results  
To evaluate the performance, accuracy, F1-score [54], and area under the receiver operating characteristic curve (ROC-AUC) scores [55] have been employed. The accuracy refers to the percentage of correctly classified unknown data samples, and \(F1\)-score computes harmonic mean between precision and recall [56]. The ROC-AUC score is measured from each classifier in the receiver operating characteristic (ROC) curve [55]. The ROC curve is used to visualize and analyze the performance of each classifier according to the various decision thresholds associated with it. The Confusion matrix shows the precise performances for the different classes. In the experiment, we have used 10-fold validation [57] to evaluate the performances according to the diverse configurations of the experimental setting. Then we have compared the performances with respect to the following factors; the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional GAP or FDAP, the five different machine learning algorithms, and an ensemble of them. The evaluation results show that the influence of augmentation on the dataset, FDAP method, and majority voting on classifier predictions boost the overall performance and reduce the rate of confusion as we expected. Table 2 illustrates the best performing results of various classifiers using two kinds of learned features with the 3x_Aug dataset. Note that the learned features from the pre-trained CNN with music have produced the comparable results with those from unsupervised CDBN that is solely trained on the cat sound data. The SVM classifier also appears good performance on CDBN features, but the AUC score does not exceed that of the ensemble classifier. The results of ensemble classifier with or without augmentation, and implementation of GAP or FDAP are presented in Table 3. Note that the performances of classifiers have been consistently improved by increasing the amount of training data with augmentation, regardless of the neural networks that are used for feature extraction. In addition, FDAP provides better performances than conventional GAP in every comparison, which proves that FDAP is more effective for the cat sound classification. The average increase in prediction accuracy on the cat sound dataset using FDAP in CNN is 7.37 and 10.05 in CDBN when 3x_Aug dataset is used for training. Table 4 shows the confusion matrix of our best performing ensemble classifier using CDBN learned feature with 3x_Aug dataset. We have examined the advantages of using FDAP method over conventional GAP by this confusion matrix. The off-diagonal numbers represent the number of cat sounds in each class that are misclassified by the ensemble classifier. After having analyzed each confusion matrix of the five classifiers and an ensemble classifier, we can reach some conclusions. The Defense (\"_Hissing_\") and HuntingMind (\"_trilling_ or _chutting_\") sounds are easily distinguishable from other cat sounds at least in our CatSound dataset, so that these classes are relatively less confusing. On the other hand, Happy (\"_meow-meow_\") and Painin (\"_miyoou_\"), have some similarity in sound \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**AUC Score**} \\ \cline{2-7}  & **CNN (\(\pm\)SD)** & **CDBN (\(\pm\)SD)** & **CNN** & **CDBN** & **CNN** & **CDBN** \\ \hline
**RF** & 84.64 (0.02) & 86.32 (0.03) & 0.85 & 0.86 & 0.988 & 0.988 \\
**KNN** & 81.60 (0.02) & 80.32 (0.02) & 0.82 & 0.80 & 0.898 & 0.890 \\
**Extra Trees** & 83.80 (0.03) & 84.29 (0.03) & 0.84 & 0.84 & 0.987 & 0.985 \\
**LDA** & 78.48 (0.02) & 81.84 (0.03) & 0.78 & 0.82 & 0.975 & 0.979 \\
**SVM** & 87.43 (0.03) & 90.88 (0.04) & 0.87 & **0.91** & 0.992 & 0.994 \\
**Ensemble** & **90.80** & **91.13** & **0.91** & **0.91** & **0.994** & **0.995** \\ \hline \hline \end{tabular} SD—Standard Deviation in each fold of cross validation.  
\end{table}