# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 2 Data Collection
### Compatibility of the Field Measurement and Simulations  
The field measurement was carried out in a classroom (approximately 136 m\({}^{2}\)) at National Cheng Kung University (Figure 3). The purpose of doing so was to confirm the compatibility of the field measurement and building acoustics simulation software in the different impacts of replacing indoor sound-absorbing materials and changing the placement of sound-absorbing materials in the same space on indoor acoustic indicators. After confirming compatibility, subsequent research and analysis were mainly carried out with the simulation software. In addition to observing the error rate of the overall data, we also compared the trend of the difference between the measured and simulated values from each receiving point individually.  
The measurement was based on ISO 3382-1. A dodecahedron speaker was used as an omnidirectional sound source. The measured parameters were sound pressure level (SPL),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Definition** & **Subj. Threshold** &
\begin{tabular}{c} **Target Octave** \\ **Band** \\ \end{tabular} \\ \hline RT (s) & The time it takes for sound to & & \\  & decay by 60 dB. & Rel. 5\% & 500 Hz \\ \hline SPL difference (dB) & The difference between the & & \\  & SPL of each point and the & – & – \\  & average value. & & \\ \hline C50 (dB) & \(10 log\frac{\frac{\partial\mu^{2}}{\partial\mu^{2}}(t)dt}{\partial\mu^{2}(t)dt}\) & 1 dB & 500 Hz \\ \hline C80 (dB) & \(10 log\frac{\frac{\partial\mu^{2}}{\partial\mu^{2}}(t)dt}{\partial\mu^{2}(t)dt}\) & 1 dB & 500 \& 1000 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target indoor acoustic indicators.  
Figure 3: Space description of field measurement: (**a**) Space size; (**b**) Space photo.  
background noise, reverberation time (T20), C50, C80, and STI. The receiving point was erected at a height of 1.5 m, and a tripod was used to move the point.  
Currently, many kinds of architectural acoustics simulation software are available, such as Odeon, EASE, CATT, etc. This study adopted Odeon (version 14) for acoustic simulation. Odeon is a piece of software developed by the Technical University of Denmark (Dept. of Acoustic Technology) and a group of consulting companies in 1984. It is capable of calculating various parameters of room acoustics based on spatial geometric conditions and surface material properties. The calculation method is a hybrid method that combines ray tracing and image sources [24].  
The 3D model was drawn based on on-site spatial surveys, and the sound absorption coefficient of material was set according to the situation of the site and related values in past literature. The referenced literature values were adjusted and confirmed within a reasonable range through Odeon's optimization function. The Genetic Material Optimizer in Odeon is an optimization tool which uses a genetic algorithm. Its function is to match the simulated room acoustic indicators with the actual measured acoustic indicators by modifying the materials in the room.