# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 2 Materials and Methods
### Data Processing
#### 2.2.1 Detection of Noise Events
In the algorithm Lr the term of +3 dB added to the running L\({}_{\text{Aeq,run}}\) was chosen because this quantity roughly corresponds to a perception of a clear difference in SPL. The algorithms L50 and O10 seem suitable for detection of \"notice-events\"; that is, those potentially attracting attention and likely causing reactions by exposed people, for instance in terms of annoyance and sleep interference [23; 26]. The algorithm O10 differs from the others because it only considers progressive positive SPL increments and not the decreasing SPL transients. The onset of 10 dB is often used as exceedance threshold to detect sound events, such as in the NAL50E10 index [16] and in the Italian legislation on railway and aircraft noise [27]. We should note that a difference of 10 dB in SPL roughly corresponds to a doubling of loudness. The output of the data processing included the following outcomes:  
* The continuous equivalent level \(L_{Aeq,T}\) in dB(A), referred to measurement time \(T\) of 10 min. * The running equivalent level L\({}_{\text{Aeq,run}}\) in dB(A). * The standard deviation (sdL\({}_{\text{A}}\)) and the kurtosis (kL\({}_{\text{A}}\)) of the 1 s short L\({}_{\text{Aeq}}\) levels during the measurement time \(T\) to describe the level distribution. * The noise climate, as difference between the percentiles levels L\({}_{\text{A10}}\) and L\({}_{\text{A90}}\) in dB(A) along the measurement time \(T\). * The intermittency ratio (IR) in %, calculated according to [17]. * The event-related component \(EVT\) of the Harmonica index [24], representing the acoustic energy provided by noise peaks that emerged above the background noise, and calculated as follows:  
\[EVT=0.25\cdot\left(L_{Aeq,T}-L_{A95 eq}\right), \tag{1}\]  
where \(L_{A95 eq}\) is the equivalent background noise level, evaluated every second by the noise level exceeded 95% of the time during the previous \(T/6\) interval, where \(T\) is the measurement time. We should note that the Harmonica index was formulated to provide information that is easier to understand by people and more closely reflects the noise nuisances, as perceived by the public. * The total number of noise events detected by the four algorithms with (C) and without (NC) conditions on event duration and time gap. * Start time \(\tau_{\text{s}}\), duration \(\tau\), SEL and L\({}_{\text{Aeq}}\) levels of each detected noise event. * Number of detected noise events. * Number of noise events due to mixed sources as recognized by the ANED procedure [19]. * Number of noise events due to road traffic noise recognized as unique labels or majority of mixed labels as provided by the ANED procedure [19]. * Number of noise events with an equal number of RTN and ANE labels. * Plot of 1 s short L\({}_{\text{Aeq}}\) levels versus time, with indication of the detected noise events and the recognized source for each algorithm. An example of the plots obtained from the data processing is given in Figure 4, regarding the noise monitoring at site Mh117 taken at 9:10 a.m., with a standard deviation sdL\({}_{\text{A}}\) of L\({}_{\text{Aeq,1s}}\) equal to 3.8 dB(A), and kurtosis kL\({}_{\text{A}}\) = 3.1, close to the value of 3 corresponding to the normal distribution of SPLs. As shown in Figure 4, the detected noise events depend on the applied algorithms, as they can be different in number, detected at different times, can have different durations, and a set of source labels that lead to different source associations; moreover, all of the methods focus on different characteristics and features from the types of noise. For instance, setting the time gap between events \(\tau_{\text{g}}\) = 5 s and the event duration \(\tau\) > 2 s, algorithm IR detects 7 noise events (6 RTN + 1 ANE), algorithm Lr detects 5 noise events (3 RTN + 1 ANE + 1 not associated source), algorithm L50 detects 1 ANE event, and algorithm O10 detects 3 RTN events. Thus, it is clear that the choice of the noise event detection algorithm is important.