# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 3 Results and Discussion
The first two dimensions together explain a large percentage (70.15%) of the dataset variability. The \(\mathrm{L_{Aeq}}\) level is the variable with the lowest contribution, confirming that this metric is not suitable to describe the presence of events. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \% & IRT5 & L\(\mathrm{T5}\) & L\(\mathrm{50T5}\) & O\(\mathrm{10T5}\) & IRT10 & L\(\mathrm{T10}\) & L\(\mathrm{50T10}\) & O\(\mathrm{OI}\)\(\mathrm{\mathrm{T10}}\) \\ \hline RTN & 78.9 & 79.2 & 75.7 & 86.0 & 79.6 & 79.2 & 74.9 & 86.2 \\ ANE & 17.9 & 17.5 & 19.9 & 11.8 & 17.4 & 17.7 & 20.5 & 11.7 \\ Mixed & 33.0 & 34.0 & 40.3 & 30.3 & 32.5 & 33.4 & 39.8 & 31.0 \\ Even & 3.1 & 3.1 & 3.9 & 2.3 & 3.1 & 3.0 & 4.1 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of events detected by the four algorithms for the two time gaps between events of 5 (T5) and 10 s (T10) as function of the source type. Figure 10: Density plots of the number of events detected in each SPL time history by the four algorithms for the two time gaps between events: (**a**) \(\tau_{\mathrm{g}}\) = 5 s (T5); (**b**) \(\tau_{\mathrm{g}}\) = 10 s (T10). The two time gaps between events led to similar results for each event detection algorithm; good correlation is observed between the intermittency ratio (IR) and the event component (EVT) of the Harmonic index, as shown in Figure 13. It is also confirmed that the algorithms L50 and O10, most likely suitable to detect notice-events, are correlated, and differ from the algorithms based on IR and Lr (see dimension 2). Considering the features of the Harmonic index, which accounts for the noise energy content (by the background component, BGN), its eventualness (by the event component, EVT), and its ease to be understood by people, we investigated its correlation with the SEL of events detected by the four algorithms. The results, in terms of linear regression, are shown in Figure 14, where the yellow and red bands on the x axis report the qualitative scale proposed for the Harmonic values [24]. All 92 SPL time histories are within the \"noisy\" and \"very noisy\" attributes of the scale. The regression is better for IR and Lr algorithms, very close to each other, than for L50 and O10. The threshold value of 8 for the Harmonic index, separating the \"noisy\" environment from the \"very noise\" environment, corresponds to different values for SEL, as shown in Figure 15. In particular, for procedures L50 and O10, suitable to detect \"notice-events\", the SEL values corresponding to the threshold value of Harmonic are lower than those observed for the IR and Lr algorithms, most likely due to the temporal patterns of such notice-events (e.g., high SPL rise time). The variability range of each reported value was obtained, considering the prediction bands at the 95% confidence level. Figure 11: Box plots of the event durations at each site detected by the IRT5 algorithm: (**a**) Andorra’s sites; (**b**) Milan’s sites. Figure 12: Correlation plot obtained by the PCA and variable contribution on a colored scale. Figure 14: Regression plots between the Harmonica index and the SEL values of the events detected by the four algorithms: (**a**) IR algorithm; (**b**) L50 algorithm; (**c**) Lr algorithm; (**d**) O10 algorithm; confidence and prediction bands at the 95% confidence level. Figure 13: Regression plot between intermittency ratio (IR) and the event component (EVT) of the Harmonica index; confidence and prediction bands at 95% confidence level.