# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 2 Materials and Methods
### Data Pre-Processing
#### 2.5.3 Audio Data Pre-Processing  
With regards to the audio data, the root mean square (RMS) sound pressure was extracted from the raw audio file, at the time window of 1, 5, 9, 15 and 46 s, respectively,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features Representing Individual Vehicle (Raw Data)** & **Input Variables for Machine-Learning Model (after Preprocessing)** \\ \hline Vehicle category & [traffic volume, ratio of motorcycle, ratio of medium vehicle, ratio of heavy vehicle, ratio of bus, ratio of car, ratio of small car] \\ Vehicle speed & [arithmetic mean, harmonic mean, min, max, median, range, mid-point, standard deviation, skewness, kurtosis] \\ Vehicle acceleration & [arithmetic mean, min, max, median, range, mid-point, standard deviation, skewness, kurtosis] \\ Vehicle deceleration & [arithmetic mean, min, max, median, range, mid-point, standard deviation, skewness, kurtosis] \\ Vehicle distance to mics & [arithmetic mean, min, max, median, range, mid-point, standard deviation, skewness, kurtosis] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The input variables of the machine-learning model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Raw Data Frame** & **Vehicle Speed** & **Vehicle Acceleration (m/s2)** & **Vehicle Deceleration (m/s2)** & **Vehicle Distance to Mic3 (m)** & **Vehicle Category** \\ \hline \multirow{5}{*}{Sample1} & & [0.0058, 0.0034, & [0.0005, 0.0012, & [0.0, 0.0, 0.0, 0.0, & [39.08, 23.87, 22.70, & [\"carcorresponding to the video data. The sound pressure was then transformed to sound pressure level (SPL dB), with the equation below:  
\[SPL=20\log_{10}(\frac{p}{2\times 10^{-5}})dB, \tag{22}\]  
where \(p\) is the RMS sound pressure, \(2\times 10^{-5}\) is the reference sound pressure in air.  
In order to have more data for model training, data from all five microphones from both days were used, since they delivered different messages (Figure 9), although the microphones were physically not that far away from each other at the roundabout.  
As shown in Figure 9, the recorded SPL curves from the five microphones from each day indicate a similar tendency, since the microphones were located at the same roundabout. However, due to the spatial distance, the shift in the peak values could still be observed, because the recorded SPL from a specific microphone at a certain time step would be dominated by the loud vehicle nearby. Due to the difference in the carried  
Figure 9: Extracted SPL every other five minutes during the two days from different microphones: (**a**) SPL every other five minutes from day 1; (**b**) SPL every other five minutes from day 2.  
messages of different microphones, it makes sense to mix and use all these data for the model training. In this regard, each microphone data is further aligned with the data of generated traffic features at each time step. For different microphones, at each time step, the paired traffic features are almost the same except for distance-related features, because the five microphones were standing in different locations.