# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 3 Machine Learning
### Data Processing  
In this study, we used the correlation matrix to preliminarily observe the relationship between the parameters. By observing the numerical distribution of each feature, we were able to obtain a rough understanding of the overall data and the applicability of this model.  
Furthermore, the discussion of the sound absorption coefficient of the material was focused on the octave bands of 500 Hz and 1000 Hz. Considering the convenience and  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Library** & **Version** & **Description** \\ \hline Jupyter notebook & 6.0.3 & Web-based execution environment \\ \hline Numpy & 1.18.1 & Dimensional array processing and mathematical operations \\ \hline Pandas & 1.0.1 & Data tabulation and numerical processing \\ \hline Matplotlib & 3.2.1 & Data visualization and graphics drawing \\ \hline Seaborn & 0.10.0 & Data visualization and graphics drawing \\ \hline Scikit-learn & 0.22.2.post1 & Machine learning model construction \\ \hline Tensorflow & 2.0.0b1 & Machine learning model construction \\ \hline \hline \end{tabular}
\end{table}
Table 6: Description of used libraries.  
Figure 8: Process of machine learning.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{6}{c}{**Absorption Coefficient, Frequency Bands (Hz)**} \\ \cline{2-9}  & **63.5** & **125** & **250** & **500** & **1000** & **2000** & **4000** & **8000** \\ \hline Smooth concrete [24] & 0.01 & 0.01 & 0.01 & 0.01 & 0.02 & 0.02 & 0.02 & 0.02 \\ \hline Carpet bonded to closed-cell & & & & & & & & \\ foam underlay [24] & 0.03 & 0.03 & 0.09 & 0.25 & 0.31 & 0.33 & 0.44 & 0.44 \\ \hline Acoustic plaster [24] & 0.15 & 0.15 & 0.25 & 0.40 & 0.55 & 0.60 & 0.60 & 0.60 \\ \hline Slotted gypsum board [24] & 0.20 & 0.20 & 0.22 & 0.71 & 0.99 & 0.55 & 0.42 & 0.42 \\ \hline Gypsum board, perforation & & & & & & & & \\
19.6\% [23] & & 0.30 & 0.69 & 1.00 & 0.81 & 0.66 & 0.62 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material setting of wall.  
feasibility of practical applications in the future, sound absorption coefficients were divided into two groups: original sound absorption coefficients and leveled sound absorption coefficients, as shown in Figure 9. We then discussed and compared the performance of these two models and determined whether the leveled sound absorption coefficients was applicable within the scope of this study.  
Categorical data, such as the location of the receiving point, type of material placement, etc., were converted into numerical data by one-hot encoding, and the leveled sound absorption coefficient was converted by label encoding. These two, namely one-hot and label encoding, are the principal methods available for converting categories or text data into numeric data. They are presented in the form of an example in Figure 10. In label encoding, each category is assigned a unique integer. Once performed, the model will consider an order or rank between categories (as shown in Figure 10: \(0<1<2\)) so that it is suitable for ordinal data. In one-hot encoding, every unique value in the category is added as a feature. This encoding method does not sort the categories and is suitable for data that are not ordinal.  
For the artificial neural network model, we adopted data normalization to scale different features to the same size, which may increase the convergence speed and improve the accuracy of the model [25; 26; 27]. In this study, the standard score (also known as \(z\)-score) was used, and it is defined by:  
\[x_{norm}=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(\mu\) is the mean of the population and \(\sigma\) is the standard deviation of the population. After this standardization action, the data had a mean value of 0 and a standard deviation of 1.  
Figure 10: Diagram of encoding method.  
Figure 9: Data processing of sound absorption coefficient.