# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 1 Introduction  
Urbanization is expected to continue in the future, causing an increase in both the number of people living in cities and the spatial densities of different noise sources. The noises in urban areas are composed of several sources (e.g., technological and anthropic sources), most of them masking natural sounds (e.g., geophonic and biophonic sounds), throughout the day. Among the abovementioned sources, road traffic noise is often predominant in urban areas, since it is widespread throughout space and time, impacting the people who are exposed to it. There is clear evidence in the literature that noise causesadverse effects on the health and well-being of citizens, e.g., sleep disturbance, annoyances, and cardiovascular diseases [1; 2; 3]. Because of this trend, many efforts have been implemented to mitigate noise impact and its harmful effects, as recommended by the environmental noise directive (END) 2002/49/EC) on the assessment and management of environmental noise [4]. Noise mapping of main sources (transport and industries) is one END requirement; its reporting to the European Commission is mandatory at least every five years. Noise maps represent--in a graphical way, through colored scale intervals--the spatial distributions of specific sound metrics, usually long-term averaged values such as day-evening-night level L\({}_{\text{den}}\) in dB(A) introduced by the END [4]. They are primarily obtained by numerical models of outdoor sound propagation; many efforts were made in recent years, in Europe, to harmonize these models [5]. The role of sound measurements is, therefore, rather limited, even if it is important to tune the model to the real environmental setting. We should note that these measurements require appropriate instrumentation and trained technicians to perform them, are time consuming and expensive, and cannot be applied on a large scale, as needed in urban areas. Recently, technological progress has led to the realization of low-cost acoustic sensors suitable to be arranged into wireless acoustic sensor networks (WASNs) [6], developed under the paradigms of smart cities and the Internet of Things (IoT). Thus, large amounts of noise data have become available for the analysis of urban noise--more detailed than that based on long-term noise metrics, such as those provided by noise maps. In 2020, a major change had occurred, due to severe restrictions on individual mobility issued by governments to tackle the COVID-19 pandemic. These restrictions have reshaped the acoustic environments in cities, and sources previously masked by road traffic noise have become audible to populations [7]. Notwithstanding the reduction of background noise levels, frequent occurrences of sound events have emerged, produced by different sources; including road vehicle passages, it is recognized that, for the same environmental conditions, human hearing is more sensitive to sound fluctuations over time rather than steady sounds (e.g., see [8]). The importance of sound's temporal structure, its audibility, and noticeability was addressed in some articles [9; 10]. The more frequent presence of sound events may increase the adverse health effects on a population, causing, for example, sleep disturbance and annoyance. Unfortunately, noise maps are not suitable tools to provide information on such critical issues because, as already mentioned, they refer to long-term average noise metrics, such as L\({}_{\text{den}}\). Some studies have dealt with this topic, e.g., [11]; moreover, different road classifications were obtained when considering noise events only [12] or together with sound energy [13]. Thus far, several methods and algorithms have been proposed to detect sound events in environmental noises. Many of them look at transients in the sound level time history, such as exceedances above fixed or time adaptive thresholds [14; 15; 16; 17]; others focus on modeling the hearing perception process of such events [9].