# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 4 Results and Discussions
### Construction of the ML Model
#### 4.2.3 Data Processing for Absorption Coefficients  
In this section, we discuss the difference in model performance between the actual sound absorption coefficient and the leveled sound absorption coefficient, where the remaining input features and target values are fixed.  
The _RMSE_ of the two methods of processing for the reverberation time is shown in Figure 19, and the _RMSE_ and \(R^{2}\) of each target value (testing set) are shown in Table 9. It can be seen that the SVM model has an obvious difference in its the prediction. With regards to reverberation time, C50, C80, and STI, the leveled absorption coefficient obtained even better results than the actual absorption coefficient.  
Figure 17: Correlation matrix of dataset.  
Figure 19: The _RMSE_ of RT by data processing for absorption coefficient.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**ANN**} \\ \cline{2-9}  & **Actual** & **Level** & **Actual** & **Level** & **Actual** & **Level** & **Actual** & **Level** \\ \hline \multirow{6}{*}{_RMSE_} & SPL & \multirow{2}{*}{0.1537} & \multirow{2}{*}{1.6254} & \multirow{2}{*}{0.4082} & \multirow{2}{*}{0.4080} & \multirow{2}{*}{0.3531} & \multirow{2}{*}{0.3558} & \multirow{2}{*}{0.2522} & \multirow{2}{*}{0.2564} \\  & difference & & & & & & & \\ \cline{2-9}  & RT at & \multirow{2}{*}{0.1517} & \multirow{2}{*}{0.1368} & \multirow{2}{*}{0.1251} & \multirow{2}{*}{0.1250} & \multirow{2}{*}{0.1131} & \multirow{2}{*}{0.1133} & \multirow{2}{*}{0.1233} & \multirow{2}{*}{0.1239} \\  & 500 Hz & & & & & & & \\ \cline{2-9}  & C50 at & \multirow{2}{*}{0.9133} & \multirow{2}{*}{0.8994} & \multirow{2}{*}{0.8430} & \multirow{2}{*}{0.8428} & \multirow{2}{*}{0.4562} & \multirow{2}{*}{0.4551} & \multirow{2}{*}{0.3522} & \multirow{2}{*}{0.3581} \\  & 500 Hz & & & & & & & \\ \hline \multirow{6}{*}{C80 at & \multirow{2}{*}{0.7812} & \multirow{2}{*}{0.7470} & \multirow{2}{*}{0.7673} & \multirow{2}{*}{0.7673} & \multirow{2}{*}{0.4358} & \multirow{2}{*}{0.4331} & \multirow{2}{*}{0.3244} & \multirow{2}{*}{0.3229} \\  & 500 Hz & & & & & & & \\ \cline{1-1} \cline{2-9}  & C80 at & \multirow{2}{*}{0.8735} & \multirow{2}{*}{0.7787} & \multirow{2}{*}{0.7511} & \multirow{2}{*}{0.7552} & \multirow{2}{*}{0.4755} & \multirow{2}{*}{0.4737} & \multirow{2}{*}{0.3518} & \multirow{2}{*}{0.3556} \\  & 1000 Hz & & & & & & & \\ \hline \multirow{6}{*}{STI} & \multirow{2}{*}{0.0641} & \multirow{2}{*}{0.0630} & \multirow{2}{*}{0.0199} & \multirow{2}{*}{0.0199} & \multirow{2}{*}{0.0110} & \multirow{2}{*}{0.0110} & \multirow{2}{*}{0.0091} & \multirow{2}{*}{0.0089} \\ \cline{1-1} \cline{2-9}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Model result by data processing for absorption coefficient.  
Figure 18: Tuning the hyperparameters of the GBDT model. (**a**) n_estimators; (**b**) max_depth.  
Figure 19: The _RMSE_ of RT by data processing for absorption coefficient.  
The other models had little difference in their processing results, which means that grading the sound absorption coefficient would not cause too much loss of data and information within the scope of this study. In practical applications, the leveled processing method would be more flexible and convenient, so subsequent research ought to focus on the leveled sound absorption coefficient.