# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 2 Materials and Methods
### Experiment Setup and Data Acquisition  
In this study, the data were collected near the town center in Blansko, Czech Republic, by video recording and audio recording over a roundabout (Figure 6). The roundabout has an inner diameter about 23.5 m and outer diameter about 38.2 m. The video recording was performed at the same time as the audio recording. A Hikvision IP camera, with a framerate of 30 fps, was installed and fixed on the roof of a 12-floor high building at the roundabout. The recording was performed over two days, with a total duration of around 9 h.  
The audio recording was performed simultaneously while video recording. Five calibrated measurement microphones were standing at different locations of the roundabout at a height of 1.2 m above the ground, as shown in Figures 6 and 7. The five microphones were connected to different channels of the same recording device, Head acoustics SQuadriga II, with a sampling rate of 48 KHz (Figure 7). All five microphones recorded simultaneously the traffic noise from the whole roundabout area. The technical details of the audio and video recording devices are listed in Table 1.  
The audio and video data were synchronized, according to the local network time, since both the obtained audio data and video data had local time tags. The camera and audio recorder were synchronized with Central European Time. The total extracted audio and video recordings have the same starting and ending time, as indicated in Table 2.  
Although the whole dataset is based on a recording of two half-days, this work focuses on the methodology of applying recurrent neural network for traffic noise modelling, for which the obtained dataset is sufficient. In future research, a longer and bigger experimental campaign is needed, covering more traffic scenarios such as different weather conditions, different road surfaces, different seasons, etc., in order to generalize the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline  & **Device Model** & **Sampling Rate** & **Number of Channels** \\ \hline Audio Recording & SQudriga II & 48 KHz & 10 \\ Video Recording & Hikvision IP camera & 30 fps & N.A. \\ \hline  & \multicolumn{3}{c}{**(b)**} \\ \hline
**Microphones** & **Frequency Response** & **Sensitivity (mV/Pa)** & **Max. Peak SPL (dB)** \\ \hline Mic3 & \multicolumn{3}{c}{51.3} \\ Mic5 & 0.02–4 KHz (\(\pm\)0.5 dB) & 51.0 \\ Mic6 & 4–20 KHz (\(\pm\)1.5 dB) & 51.6 \\ Mic7 & 51.9 \\ Mic8 & 52.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical details of recording devices: (a) info of audio and video recorders: (b) info of microphones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Start** & **End** & **Total Duration** \\ \hline Day 1 (30 September 2019) & 16:43 p.m. & 20:32 p.m. & 3 h 49 min \\ Day 2 (1 October 2019) & 06:34 a.m. & 11:48 a.m. & 5 h 14 min \\ \hline Day 1 + Day 2 & - & - & 9 h 3 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: The extracted recording times.  
Figure 7: The audio recording setup: (**a**) indication of microphone locations; (**b**) picture of instruments used for audio recording.