## 4 Experiment and Results
### Exp. #2: Comparison of Overlapped and Non-Overlapped Filters  
The newly-developed methodology proposed in this paper is the use of bispectrum analysis and a 3D pyramid filtering for the feature extraction subsystem. The idea of using a 3D pyramid filter as the filtering method is to emphasize the values in the center of the filter and devote less attention to the values at the edges. As Table 1 shows, the mean percentage (Mode #4) with 5 \(\times\) 5 non-overlapping filters provides a good recognition rate. To improve the recognition rate, the number of filters used for feature extraction was increased to 7 \(\times\) 7 filters with overlapping filters at the base of the pyramid model. The hypothesis is that by using overlapping filters, the values at the edge of the filters, which have less importance in one filter, will be given higher importance in the adjacent filters; therefore, it is expected that every element of the bispectrum matrix has equal importance. In this experiment we assessed whether the increasing number of the filter will increase the recognition rate. However, we only compare the 5 \(\times\) 5 filters with the 7 \(\times\) 7 filters as the first step, and if the higher number of filters results in a higher recognition rate, then the number of filters could be increased more. In this experiment, we only used the mean percentage feature (Mode #4), following our findings in the previous experiment. Each feature type is dimensionally reduced using PCA with 99% Eigen values. The BPNN was used as the benchmark classifier, and the recognition rate result is presented in Table 3.  
Table 3 shows that the 5 \(\times\) 5 non-overlapping filters gave a higher recognition rate than that of the 7 \(\times\) 7 overlapping filters, which deviates from our hypothesis. The superiority of the 5 \(\times\) 5 non-overlapping filters is obvious for the arousal emotion, with a difference up to 10%. One-way ANOVA was used to compare the recognition rate of using the different filter types in the feature extraction process (5 \(\times\) 5 non-overlapping filters and 7 \(\times\) 7 overlapping filters). The resultant \(p\)-value of the combined arousal and valence emotion recognition rate was \(p=0.126\) (\(p>0.05\)), which implies \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pairwise**} & & **Arousal** & & & **Valence** & \\ \cline{2-7}  & **Q-Statistic** & \(p\)**-Value** & **Inference** & **Q-Statistic** & \(p\)**-Value** & **Inference** \\ \hline Mode \#1 vs. Mode \#2 & 4.611 & 0.0015 & \(p<0.05\) & 3.578 & 0.0121 & \(p<0.05\) \\ Mode \#1 vs. Mode \#3 & 3.973 & 0.0054 & \(p<0.05\) & 3.940 & 0.0058 & \(p<0.05\) \\ Mode \#1 vs. Mode \#4 & 3.605 & 0.0114 & \(p<0.05\) & 0.434 & 0.9716 & \(p>0.05\) \\ Mode \#2 vs. Mode \#3 & 0.6375 & 0.9183 & \(p>0.05\) & 0.3620 & 0.9832 & \(p>0.05\) \\ Mode \#2 vs. Mode \#4 & 8.2161 & 0.0000 & \(p<0.05\) & 4.0122 & 0.0050 & \(p<0.05\) \\ Mode \#3 vs. Mode \#4 & 7.579 & 0.0000 & \(p<0.05\) & 4.374 & 0.0024 & \(p<0.05\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of statistical significance of Tukey–Kramer HSD test in the arousal and valence recognition rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Filter Type**} & \multicolumn{2}{c}{**Maximum Recognition Rate 1**} & \multicolumn{2}{c}{**Mean \(\pm\) SD Recognition Rate**} \\ \cline{2-5}  & **Arousal (\%)** & **Valence (\%)** & **Arousal (\%)** & **Valence (\%)** \\ \hline
5 \(\times\) 5 non-overlapping filters & 74.22 & 77.58 & **72.83 \(\pm\) 4.04** & **70.95 \(\pm\) 9.86** \\
7 \(\times\) 7 overlapping filters & 64.22 & 73.13 & **61.14 \(\pm\) 3.18** & **72.81 \(\pm\) 0.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition rate results from different filter types. that neither of the filter types were significantly different. However, the \(p\)-value for arousal emotion recognition rate was \(p\) = 0.001 (\(p\) < 0.05), and the valence emotion recognition rate was \(p\) = 0.685 (\(p\) > 0.05).