# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 5 Conclusions  
In this study, we focused on the current deficiencies or complicated and time-consuming processes of the current prediction of indoor acoustic indicators and constructed an inno \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON]’s** & **[PERSON]’s** & **[PERSON]’s** & **GBDT** \\  & **Equation** & **Equation** & **Equation** & **Model** \\ \hline _RMSE_ & 0.739 & 0.913 & 0.775 & 0.022 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The _RMSE_ of reverberation time for 800 spaces. Figure 22: _JND_ distribution histogram of reverberation time: (**a**) GBDT model; (**b**) Traditional estimation formulas. vative prediction model. It is quick and simple and does not require 3D modeling. In the practical applications of architectural design, it could more effectively and conveniently evaluate the relevant acoustic indicators and also has a certain degree of accuracy. After discussion and analysis, the conclusions of this study are summarized as follows:  
* Data processing of sound absorption coefficients In terms of the performance of the model, with the exception of SVM, the differences in the results of the actual and leveled sound absorption coefficients are quite small. Substituting the leveled sound absorption coefficient for the actual sound absorption coefficient is a feasible, flexible, and convenient option. * Correlation analysis and feature selection of ML In this study, spatial geometric properties are highly correlated with acoustic indicators. Surprisingly, the correlation between the equivalent sound absorption area and acoustic indicators is low. However, in the ML model, deleting the less relevant parameters does not have good performance. In ML, various parameters may interact with each other and then affect the final prediction. Therefore, when adjusting the input features of the model, more detailed comparisons and judgments are required. In addition, the input combination of features can also be explored through methods such as sequential feature algorithms, which are used for dimensionality reduction. * Results of the machine learning predictive models Except for the reverberation time, the ANN model exhibited the best performance. The _absolute error_ of the SPL distribution difference fell mostly within \(\pm 0.5\) dB, while the _JND_ of C50, C80, and STI were all within \(\pm 1\). In terms of the reverberation time, the GBDT model performed the best. It is also comparable to the traditional estimation formula commonly used in the past. We found that the predictive ability of the GBDT model is much higher than the traditional formula, is convenient in practical applications, and can be quickly and effectively evaluated in the architectural design stage. The main research object of this study was a multi-functional activity center with a fixed space. All the training data were generated by acoustic simulation software. The premise of the model in this study is a fixed spatial form, and the variation of material placement is limited to 6 types. Therefore, the space or material placement that falls outside of the conditions of this study set is not suitable for the prediction model proposed in this study. In addition, the applicability of space area not in the dataset of this study needs more discussion and research. Regarding the actual field measurement, although this study carried out a compatibility comparison, its accuracy requires further discussion and research. The applicability of the model would become more credible if it could be trained with actual field measurement data. However, considering that collecting acoustic measurement data is often difficult and expensive, obtaining a large amount of real data is not necessarily feasible. It is suggested that follow-up research may try to use the transfer learning method of ML, which often uses simulated data to train the model, and then use a small amount of real-field data to adjust the model.