## 4 Results

Table 2: Best performances of various classifiers on CNN and CDBN feature extracted from 3x_Aug dataset with FDAP. Bold numbers mark the highest score of the algorithm(s) for learned features from network. features so that all classifiers decide these classes more confusing. Likewise, warning (\"_growling_\") is easily confused with Mating (\"_gay-gay-gay_\"). The ROC curves of five classifiers and an ensemble classifier and their corresponding ROC-AUC scores are illustrated in Figure 8. One can observe that the ensemble classifier produces the closest performance to the ideal classifier, even though SVM shows the similar performance. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Angry** & **Defense** & **Fighting** & **Happy** & **Hunting** & **Mating** & **Mother** & **Paining** & **Resting** & **Warning** \\ \hline Angry & **83 */94** & 2/– & 2/– & –/– & 2/1 & 1/– & 4/1 & –/– & 7/4 \\ Defense & 1/– & **92/97** & –/– & –/– & 4/1 & 1/3 & 1/– & –/– & –/– & 1/– \\ Fighting & 1/1 & 2/– & **84/90** & 1/– & 7/5 & 1/1 & –/2 & 3/1 & –/– & 1/1 \\ Happy & 4/2 & 4/2 & 3/– & **74/90** & 5/1 & 1/– & 1/1 & 1/1 & –/– & –/– \\ HuntingMind & 1/– & 4/– & 4/1 & –/– & **78/96** & 6/1 & –/– & -/– & 2/– & 6/2 \\ Mating & 2/2 & –/– & 2/1 & –/2 & 7/3 & **76/85** & 1/– & 2/2 & 2/– & 8/5 \\ MotherCall & 1/– & –/– & 2/– & 4/2 & 4/2 & 3/1 & **78/94** & 4/– & 5/2 & –/– \\ Paining & 7/5 & 1/– & 3/1 & 8/4 & 1/2 & 1/– & 4/3 & **75/85** & –/– & –/- \\ Resting & –/– & 3/3 & 1/– & –/– & 6/3 & 4/– & 1/– & –/– & **78/92** & 7/1 \\ Warning & 5/2 & 5/2 & 2/– & 1/2 & 4/2 & 5/2 & –/– & 1/1 & 2/1 & **76/89** \\ \hline \hline \end{tabular}  
* Number of cat sounds fall in same or other class using GAP; # Number of cat sounds fall in same or other class using FDAP.  
\end{table} 
Table 4: The confusion matrix of the best performing ensemble classifier on 3x_Aug dataset using CDBN features with GAP and FDAP. The first number represents the number of confused cat sounds using GAP and second using FDAP, illustrated as GAP/FDAP format. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**AUC Score**} \\ \cline{2-7}
**Cat Sound Dataset** & **CNN** & **CDBN** & **CNN** & **CDBN** & **CNN** & **CDBN** \\ \hline
**Original\_GAP * & 70.71 & 76.09 & 0.690 & 0.760 & 0.958 & 0.963 \\
**Original\_FDAP \#** & 79.12 & **82.15** & 0.780 & **0.820** & 0.974 & **0.982** \\
**1x\_Aug\_GAP** & 80.61 & 76.39 & 0.810 & 0.760 & 0.977 & 0.970 \\
**1x\_Aug\_FDAP** & 86.51 & **87.52** & 0.860 & **0.880** & **0.989** & **0.989** \\
**2x\_Aug\_GAP** & 81.89 & 76.60 & 0.820 & 0.760 & 0.982 & 0.969 \\
**2x\_Aug\_FDAP** & **89.31** & 87.96 & **0.890** & 0.880 & **0.993** & 0.991 \\
**3x\_Aug\_GAP** & 83.04 & 79.49 & 0.830 & 0.790 & 0.985 & 0.977 \\
**3x\_Aug\_FDAP** & 90.80 & **91.13** & **0.910** & **0.910** & 0.994 & **0.995** \\ \hline \hline \end{tabular}  
* GAP—Globalaverage pooling; # FDAP—Frequency division average pooling.  
\end{table} 
Table 3: The accuracy, F1-Score and area under ROC curve comparison of CNN and CDBN feature using ensemble classifier on original and augmented datasets. The advantage of FDAP over GAP in each layer feature map of two networks using various datasets are denoted by “GAP” and “FDAP” at the end of corresponding dataset. Bold numbers mark the best-performing algorithm(s) for a dataset using FDAP in a learned feature of the network.