## 4 Discussion and Conclusions  
This study attempted to use different models to predict the sound absorption coefficient of the MEMM and discuss its applicability. After comparing the conventional model and the machine learning model, the machine learning model is more accurate with regard to predictive ability. As for use, the geometric conditions of materials used in machine learning are simple to obtain, while traditional models require microphotograph and conversion methods for orifice diameter.  
In the first resonance frequency band, the predictive ability of the conventional model was not poor, but regardless of the model, there was several resonance frequency bands (several resonance frequencies), which are not show in the experimental results. The assumptions of the conventional model were all speculated to be ideal physical environments. In the literature, the model was compared with the measurement of impedance tubes to demonstrate the results of several resonance frequency bands [3]. However, the results of this study, whose experiment was conducted in a reverberation room, has no such situation. The measurement method of sound absorption consists of small-size--impedance tubes [20; 21] and large-size--reverberation room [11]. Further research is warranted to clarify the difference and connection between the two.  
The ML model did not have this problem because the sound absorption coefficient of the training set was obtained from the reverberation room experiment. In the comparison of each ML model, the average model was usually found to underestimate the sound absorption capacity of the MEMM at the resonance frequency. It was speculated that during the average process, the result was affected by the poorly performing linear model. Furthermore, the predictive ability still differed between the training set and the testing set. If the number of the entire data set is increased, the model could be further improved to decrease the performance gap between the two.  
The generalization of machine learning models primarily depends on the data set. Therefore, its predictive ability is more credible within the scope of the experiment of this study. For other types of MEMM, their applicability remains to be studied. Furthermore, if the number and diversity of experimental specimen are increased in the future, the scope of application of the prediction models can be continuously increased.  
Conceptualization, Y.-S.T.; methodology, Y.-S.T. and C.-Y.Y.; software, C.-Y.Y.; validation, Y.-S.T. and C.-Y.Y.; data curation, Y.-S.T. and C.-Y.Y.; writing--original draft preparation, C.-Y.Y.; writing--review and editing, Y.-S.T.; visualization, C.-Y.Y.; supervision, Y.-S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Model** & **RMSE** \\ \hline \multirow{4}{*}{Training set} & Lasso & 0.069 \\  & ENet & 0.069 \\  & KRR & 0.070 \\  & Gboost & 0.033 \\  & Average & 0.056 \\  & Stack & 0.040 \\ \hline \multirow{4}{*}{Testing set} & Lasso & 0.092 \\  & ENet & 0.092 \\  & KRR & 0.095 \\  & Gboost & 0.062 \\  & Average & 0.081 \\  & Stack & 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMSE of ML models.