# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 1 Introduction
A review of the wide range of algorithms, protocols, or criteria reported on in the literature for identifying noise events within a time series of A-weighted sound levels, usually from road, rail, or aircraft sources, is given in [15]. In particular, in [16], a small set of parameters were identified, which may prove useful in the construction of event-based indicators supplementary to energy-equivalent measures of road traffic noise. A further approach is the detection of noise \"notice-events\", which, for their features, are clearly perceived and potentially affect \"exposed people\". On this issue, the model proposed in [9] considers aspects of human auditory perception, such as attention strength and habituation of the time constant; it is grounded in the hypothesis that long-term perception of environmental sound is determined primarily by short notice-events. Thus, the detection of noise events is strongly required to guide noise mitigation actions; it clearly requires automatic procedures [18]. Within the above-mentioned framework, another issue that should be further developed is the recognition of the source generating the sound event. Several studies on soundscape have shown that the human response to sound events depends not only on the level, but also on the type of noise source--natural sources are seen as more acceptable than the mechanical sources. Once more, automatic procedures that are able to detect the type of the sound source, discriminating road traffic from other sources, are strongly needed. The procedure developed in [19] showed promising results and was applied in some noise monitoring networks [20]. In this paper, criteria to detect sound events were applied to the acoustic monitoring data collected in urban areas. Moreover, to automatically recognize the sound source producing the noise event, the anomalous noise event detection (ANED) procedure [19] was applied to estimate whether the source was a road traffic noise or another source. The outcomes of both detection and recognition procedures can provide useful hints to improve the efficiency of noise mitigation actions. Noise event detection algorithms were applied to the sound monitoring data taken at three sites in Andorra la Vella, Principality of Andorra [21], and three sites in Milan, Italy [22]. At each site, the 1 s A-weighted short L\({}_{\text{Aeq,1s}}\) time history, without any temporal weighting (e.g., fast or slow), lasting 10 min, was available for each hour, from 8 a.m. to 7 p.m. The 92 collected outdoor L\({}_{\text{Aeq,1s}}\) time histories covered a reasonable range of urban environmental noise time patterns within the time period examined. Another important issue was to quantify the adverse health effects on the population exposed to the noise events and the sound energy. For such a task, it was fundamental to relate the outcomes from the population with noise descriptors. Many studies and reviews deal with this important topic (e.g., see [3]). Regarding annoyance from transportation noise, a large study performed in Switzerland led to exposure-response relationships of the percentage of highly annoyed people (%HA), as functions of road traffic, railway, and aircraft noise exposure, measured a the day-evening-night level (L\({}_{\text{den}}\)), and used to clarify the degree to which the acoustic indicator intermittency ratio (IR)--which describes the eventfulness of a noise situation [17]--predicts noise annoyance [23]. Within this framework, one interesting indicator is the Harmonic index [24], developed to provide information on environmental noise that is closer to what people perceive, making it easier for the general public and authorities to be understood. This indicator considers both the continuous and the sporadic nature of noise, by including the background noise (BGN) and the noise event (EVT) components.