## 4 Simulation Results
### Model Selection
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Layer** & **Number of Filter** & **Filter** & **Stride** & **Padding** \\ \hline Input & [576 1 1] of valence; [336 1 1] of arousal & & & \\ \hline Convolutional (+BN,Relu) & 10 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 11 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 11 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 11 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Maxpooling & & [10 1] & [2 2] & [4 5 0 0] \\ \hline Convolutional (+BN,Relu) & 20 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 20 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 20 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 20 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Maxpooling & & [10 1] & [2 2] & [4 5 0 0] \\ \hline Convolutional (+BN,Relu) & 40 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 40 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 40 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Maxpooling & & [10 1] & [2 2] & [4 5 0 0] \\ \hline Convolutional (+BN,Relu) & 80 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 80 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Convolutional (+BN,Relu) & 80 & [100 1] & [1 1] & [50 50 0 0] \\ \hline Maxpooling & & [10 1] & [2 2] & [4 5 0 0] \\ \hline First Fully connected & Output sizes: 100 & & & \\ \hline \hline \end{tabular} BN: BatchNomalization layer.  
\end{table} 
Table 3: The selected structure of the CNN extractor. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Valence**} \\ \hline
**Model** & **Feature Set** & **Ac (\%)** & **Pn (\%)** & **Re (\%)** & **F1 (\%)** \\ \hline KNN (K = 37) & SCFs & 65.35 \(\pm\) 0.53 & 65.62 \(\pm\) 0.23 & 98.96 \(\pm\) 0.97 & 78.69 \(\pm\) 0.50 \\ \hline BG tree = 15, leaf = 5 & SCAF & 40.65 \(\pm\) 2.61 & 63.42 \(\pm\) 7.71 & 29.41 \(\pm\) 7.25 & 34.79 \(\pm\) 6.74 \\ \hline BS \(\text{lr}=0.9\), \(\text{iter}=50\), \(\text{min\_leaf}=75\) & TIF & 62.00 \(\pm\) 2.40 & 66.86 \(\pm\) 1.22 & 83.15 \(\pm\) 4.12 & 73.63 \(\pm\) 1.96 \\ \hline RF tree = 10, leaf = 5 & SCFs & 47.15 \(\pm\) 2.75 & 61.27 \(\pm\) 2.62 & 54.87 \(\pm\) 6.69 & 55.15 \(\pm\) 4.56 \\ \hline CNN \(\text{Nd}=3\), \(\text{Ns}=2\), \(\text{lr}=0.01\), \(\text{mo}=0.9\), \(\text{l}2=0.1\) & TIF & 67.75 \(\pm\) 6.33 & 67.03 \(\pm\) 7.11 & 99.17 \(\pm\) 1.14 & 79.83 \(\pm\) 5.12 \\ \hline CNN \(\text{KNN}=3\) & TIF & 68.38 \(\pm\) 0.98 & 68.24 \(\pm\) 0.43 & 96.76 \(\pm\) 2.32 & 80.03 \(\pm\) 0.92 \\ \hline CNN \(\text{HC}=5\), \(\text{leaf}=75\) & TIF & 58.63 \(\pm\) 3.45 & 76.53 \(\pm\) 2.51 & 53.13 \(\pm\) 5.86 & 62.72 \(\pm\) 4.86 \\ \hline CNN \(\text{HC}=5\) & SCFs & 70.43 \(\pm\) 1.48 & 69.88 \(\pm\) 0.33 & 98.70 \(\pm\) 2.44 & 81.83 \(\pm\) 1.23 \\ \hline CNN \(\text{HC}=5\) & SCFs & 60.61 \(\pm\) 2.89 & 86.42 \(\pm\) 5.74 & 48.06 \(\pm\) 5.82 & 61.77 \(\pm\) 3.82 \\ \hline \multicolumn{5}{c}{**Arousal**} \\ \hline KNN K = 5 & TIF & 63.70 \(\pm\) 0.41 & 63.78 \(\pm\) 0.25 & 96.66 \(\pm\) 1.26 & 76.38 \(\pm\) 0.28 \\ \hline BG tree = 15, leaf = 5 & SCFs & 55.17 \(\pm\) 3.53 & 68.17 \(\pm\) 2.17 & 53.72 \(\pm\) 7.02 & 57.87 \(\pm\) 5. For the arousal scenario, the optimal values are K of 5 for the KNN; tree number of 15 and leaf number of 5 for the BG; learning rate of 0.9, iteration of 50, and minimum leaf of 75 for the BS; tree number of 10 and leaf number of 5 for the RF; network depth of 2, network section of 3, learning rate of 0.005, momentum of 0.9 and L2 regulation of 0.15 for the CNN. The four ML models, which are combined with the above extractor, are given the optimal parameter values by the grid search method, such as KNN with K of 53; BG and RF with tree number of 3 and leaf number of 5; BS with learning rate of 0.1, iteration of 50, and minimum leaf of 105.