# GCE: An Audio-Visual Dataset for Group Cohesion and Emotion Analysis
## 5 Experimental Results
### Performance  
The evaluation of model performance involves the simultaneous goals of forecasting group cohesion and identifying group emotions. Various quantitative assessment metrics were utilized to evaluate the effectiveness of the models in forecasting group cohesion. These metrics include mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and R-squared (\(R^{2}\)). The results of predicting group cohesiveness are presented in Table 7. In the single-modality experiment, the model using a spectrogram as a feature showed the best performance. This resulted in an MAE of 0.547, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Face\_1** & **Face\_2** & **SlowFast** & **OpenSMILE** & **Wav2 Vec 2.0** \\ \hline Input: 64 \(\times\) 100 \(\times\) 512 & Input: 64 \(\times\) 100 \(\times\) 1408 & Input: 64 \(\times\) 2304 & Input: 64 \(\times\) 88 & Input: 64 \(\times\) 1024 \\ AvgPool: 64 \(\times\) 100 & AvgPool: 64 \(\times\) 100 & FC1: 64 \(\times\) 100 & FC1: 64 \(\times\) 100 & FC1: 64 \(\times\) 100 \\ FC1: 64 \(\times\) 64 & FC1: 64 \(\times\) 64 & FC2: 64 \(\times\) 64 & FC2: 64 \(\times\) 64 & FC2: 64 \(\times\) 64 \\ FC2: 64 \(\times\) 32 & FC2: 64 \(\times\) 32 & FC3: 64 \(\times\) 32 & FC3: 64 \(\times\) 32 & FC3: 64 \(\times\) 32 \\ Output1: 64 \(\times\) 3 & Output1: 64 \(\times\) 3 & Output1: 64 \(\times\) 3 & Output1: 64 \(\times\) 3 & Output1: 64 \(\times\) 3 \\ Output2: 64 & Output2: 64 & Output2: 64 & Output2: 64 & Output2: 64 \\ \hline
**Spectrogram** & **Fusion (Visual)** & **Fusion (Audio)** & **Fusion (Visual + Audio)** \\ \hline Input: 64 \(\times\) 128 \(\times\) 1292 & & & \\ SE1: 64 \(\times\) 64 \(\times\) 323 & & & \\ SE2: 64 \(\times\) 32 \(\times\) 80 & & & \\ SE3: 64 \(\times\) 16 \(\times\) 40 & & & \\ SE4: 64 \(\times\) 8 \(\times\) 20 & & & \\ AvgPool: 64 \(\times\) 8 & & & \\ FC1: 64 \(\times\) 64 & & FC1: 64 \(\times\) 64 & & \\ FC2: 64 \(\times\) 32 & & FC2: 64 \(\times\) 32 & & \\ Output1: 64 \(\times\) 3 & & Output1: 64 \(\times\) 3 & & Output1: 64 \(\times\) 3 \\ Output2: 64 & & Output2: 64 & Output2: 64 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Models’ settings. Output1 is the emotional classification, Output2 is the cohesion prediction. an MAPE of 0.173, an MSE of 0.864, and a coefficient of determination \(R^{2}\) of \(-\)0.022. In addition, the model using _Face_2_ as a feature showed a low error rate in terms of MAE, MAPE, MSE, and \(R^{2}\). The proposed baseline model, fusion with visual and audio showed a high performance, exhibiting the lowest error rates in cohesiveness prediction with an MAE of 0.077, an MAPE of 0.116, and an MSE of 0.012, respectively. The performance metrics of the Fusion (Visual + Audio) model's MAE, MAPE, MSE, and R-squared (R\({}^{2}\)) metrics on the test dataset are shown in Figure 10. Based on the test data, we observed the error between the predicted and true labels. In the case of the Fusion (Visual + Audio) model, this showed a relatively high performance on MAE and MSE. We used accuracy (Acc), precision (Pre), recall (Rec), F_1 score, average precision (AP), and area under the receiver operating characteristic curve (AUC) as classification evaluation metrics. The baseline results are shown in Table 8. In a single-modality experiment, the model using _SlowFast_ as a feature showed a high performance with a precision of 0.33 and a recall of 0.35, and the model using a spectrogram as a feature showed a high performance with an AP and AUC of 0.53 and 0.73, respectively. The proposed baseline model, which fuses visual and audio features, achieved the highest performance with an accuracy of 91% and an \(F_{1}\) score of 0.32. This confirmed that a model that combines visual and audio features is more effective for classifying group emotions than a model that uses a single modality.