# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 4 Conclusions  
The evaluation of human reactions to road traffic noise exposure might be improved by accounting for the occurrence of noise events in addition to the use of indicators based on sound energy. A wide range of procedures to detect such noise events were proposed in the literature, but there is yet no generally accepted algorithms. Considering the outcomes of the literature on this important topic, it was deemed of interest to examine four algorithms for noise event detection, as described in Figure 3--IR, Lr, L50, and O10. To evaluate the performance of these algorithms, they were applied to the sound monitoring data taken at six sites in urban areas, formed by 92 L\({}_{\text{Aeq1s}}\) time histories. Three options were considered for the event detection--no condition on event duration \(\tau\) and time gap between adjacent events \(\tau_{\text{g}}\), event duration \(\tau>2\) s with time gap \(\tau_{\text{g}}=5\) s, event duration \(\tau>2\) s and time gap \(\tau_{\text{g}}=10\) s. Moreover, the available data are able to label each 1 s short L\({}_{\text{Aeq}}\), accord dating to the binary classification obtained by the anomalous noise event detection (ANED) procedure, and discriminate road traffic noise (RTN) from other sources (ANE). This is a very important feature that improves the efficiency of noise mitigation actions, focusing on specific sources. The results showed that the algorithms L50 and O10, owing to their definitions, seem more suitable to detect \"notice-events\", e.g., events that, for their features, are clearly perceived and potentially affect the people who are exposed to them. These algorithms detect much less events than those recognized by the other two algorithms, namely IR and Lr. For all of the algorithms, the site in Milan with high traffic flow showed a number of detected events lower than those observed for moderate (MmT) and low (MIT) traffic flow, most likely due to the louder background noise produced by the higher number of vehicles passing-by. The conditions required for the events largely reduced the number of those recognized, with the exception of the O10 algorithm. In terms of noise exposure, the SEL values for the conditioned events were lower than those observed for not conditioned events. The ANED procedure showed that road traffic noise was largely the source producing the events (more than 70% of detected events for all algorithms). The principal component analysis showed that the two time gaps between events led to similar results for each event detection algorithm; moreover, good correlation was observed between the intermittency ratio (IR) and the event component (EVT) of the Harmonic index. Regarding this index, its linear regression with the SEL of events detected by the four algorithms provides interesting results. In particular, the value of 8 for the Harmonic index, separating the \"noisy\" from the \"very noise\" environments on the qualitative scale, corresponds to SEL values of about 92 dB and 88-89 dB for the IR and Lr couple and the L50 and O10 couple, respectively. This difference is most likely due to the different selections  
Figure 15: Linear regression fitting between the Harmonic index and the SEL values of events detected by the four algorithms; SEL values corresponding to Harmonic index = 8 are reported, with their variability range obtained by the prediction bands at the 95% confidence level. of events performed by the algorithms, with L50 and O10 more suitable for notice-events, characterized by specific temporal patterns (e.g., high SPL rise time). The results cannot be generalized and are limited to the four algorithms considered. However, it seems that the L50 algorithm, providing the lowest number of events, is the least sensitive in detection; the performance of the O10 algorithm is less influenced by conditions required for noise events than observed for the other three algorithms.