## 4 Sound Quality Prediction
### Comparison of Prediction Results  
To evaluate the effect of the prediction model more directly, mean absolute percentage error (MAPE), coefficient of determination (R\({}^{2}\)), and residual predictive deviation (RPD) are used as evaluation indexes. Among the three evaluation indexes, MAPE is used to compare the prediction accuracy of the three models, R\({}^{2}\) is used to represent the fitting degree of the three models, and RPD is used to measure the generalization ability and stability of the three models. MAPE is a percentage that represents the average relative difference between the predicted error and the true value. A lower MAPE value indicates that the model's predictions are more accurate. Based on MAPE, we can compute the prediction accuracy as follows:  
\[MAPE=\left(\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}} \right|\right)\times 100\% \tag{5}\]  
\[Accuracy=1-MAPE \tag{6}\]  
where \(n\) is the number of samples, \(\hat{y}_{i}\) is the predicted value of the sample, and \(y_{i}\) is the true value of the sample. On the six datasets, we trained the three models with seven acoustic parameters as inputs and subjective evaluation scores as outputs, and the optimal parameters of each model are substituted by Tables 6-8 above. The comparison of prediction accuracy on the three models is shown in Figure 8.  
As can be seen from Figure 8, on the new data set generated by fuzzy generation, the prediction accuracy of the three models all showed a downward trend with the decrease of membership value. Among the three models, the GRNN model has more advantages than the other two models. Although the accuracy of the GRNN model decreases with the decrease in membership value, the decline trend is more uniform. When the membership value is less than 0.5, the advantages of the GRNN model are more prominent. For the SVR model, when the membership value is not less than 0.5, its prediction accuracy is close to the GRNN model, and sometimes even a little better. However, when the membership value is less than 0.5, the accuracy of the SVR model drops sharply. When the membership value is not less than 0.3, the prediction accuracy of the SVR model is obviously greater than that of the RR model, but when the membership value is a minimum of 0.1, the prediction accuracy of the SVR model is the lowest. For the RR model, the changing trend of its \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Membership Value** & **1** & **0.9** & **0.7** & **0.5** & **0.3** & **0.1** \\ \hline Best \(c\) & 70.6038 & 36.8970 & 87.3501 & 0.5924 & 33.3870 & 5.5020 \\ Best \(g\) & 0.1895 & 5.5175 & 6.0702 & 3.7348 & 0.8873 & 1.1279 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimization results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Membership Value** & **1** & **0.9** & **0.7** & **0.5** & **0.3** & **0.1** \\ \hline \(\lambda\) & 0.0450 & 0.0450 & 0.0047 & 0.2442 & 0.4292 & 12.6486 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best values of \(\lambda\). prediction accuracy is basically the same as the SVR model. Though the decline trend is slower, the RR model is always weaker than the SVR model when the membership value is not less than 0.3. In Figure 8, compared with training on the original dataset (i.e., when the membership value is equal to 1), when the membership value is not less than 0.5, the use of the fuzzy generation method greatly improves the prediction accuracy of the models. When the membership value is 0.3, the accuracy of the GRNN model and the SVR model is still greater, but the RR model is no longer better. When the membership value is 0.1, the accuracy of the three models is worse than that trained under the original dataset, and the accuracy of the SVR model is the lowest. In general, the results show that rational use of fuzzy generation can improve the prediction accuracy of models.