# Noise Prediction Using Machine Learning with Measurements Analysis
## 2 Materials  
### Information Introduction and Data Analysis  
The noise data were provided by the NSRRC and contained more than 13,000,000 samples covering the time period from 08:00 on 1 February 2019 to 23:59 on 31 August 2019. The NSRRC installed 12 noise detection sensors around the work environment. As shown in Figure 2a, it showed the circle building has a 120 m circumference, and 24 straight line experimental stations that are differential function research experiments; the locations of the sensors were divided into the inner circle (1-6) and the outer circle (7-12), shown as green dots. In addition, there are many noise sources from vacuum pumps, cooling pumps, liquid nitrogen pressure relief, computer servers, etc. in the working environment, as shown in Figure 2b. In this study, the noise sensor module had more details including the measuring range, frequency range, weighing, and resolution, as shown in Figure 2c. This sensor device can instantly convert sound into \(\mathrm{Leq}\) information in the cloud as shown in Figure 2d. Due to the limitations of the hardware and network, the \(\mathrm{Leq}\) was recorded once per second and the average \(\mathrm{Leq}\) was uploaded once every 10 s. Using this device, the different frequencies for \(\mathrm{Leq}\) values were collected. The data were divided into eight different frequencies: 125 Hz, 250 Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz, 8 kHz, and 16 kHz. Furthermore, the noise monitoring system that provided sensors, the location, time and different frequency noise data are shown in Figure 2e. An alternative function in our monitoring system showed 12 groups for real-time linear charts that can be displayed within two hours' data, as shown in Figure 2f. The segmentation for a maximum date interval of 12 group's linear charts was set as a month, as shown in Figure 2g. Based on this system, we kept the data collection for further analysis in future work. Figure 1: The main process of the method in three parts. Figure 2: (**a**) Noise sensor map. (**b**) Noise working environment of the National Synchrotron Radiation Research Center (NSRRC). (**c**) Noise sensor module. (**d**) Noise detector and location. (**e**) Noise monitoring system illustrating. (**f**) Two hours’ real-time detection chart. (**g**) One-month detection chart. As the inner and outer ring sensors had different installation times, we divided the statistics of the Leq for the 12 sensors as shown in Figure 3. The data collected from sensors 1-6 were recorded from February to August; the remaining sensors collected data from April to August. The detailed time distribution and the number of data points are shown in Figure 3. In addition, all sensors had relatively complete data in July and August; thus, we used the most recent August data for training in the experiment. Figure 4 shows the distribution of the average Leq levels of each sensor at 125 Hz and 1000 Hz over the different months (for the other monthly statistics from different frequencies, see Appendix A). It is evident that the average Leq level for sensor 8 was higher than that of the other sensors and closer to 70 dBA from 125 to 1000 Hz for each month. As shown in Figure 5, there was no sensor with a particularly prominent value from 2000-16 kHz, indicating that the low frequencies were the main noise sources in the environment. Therefore, we hypothesized that when the equipment was operating, it caused louder low-frequency noise near sensor 8. In fact, there were more noise sources near sensor 8 than in the other areas, which caused dBA values higher than those of the other sensors. Figure 4: Average at 125 Hz and 1000 Hz in different months for different sensors. Figure 3: Twelve noise sensors collected data in different months. Next, we measured each sensor's average daily data on a single frequency band over one week. The Leq value for the 125 Hz band was higher than that of the other frequencies, as shown in Figures 6 and 7 (for the other daily statistics from different frequencies, see Appendix B).