Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonica Index

Abstract

Noise annoyance depends not only on sound energy, but also on other features, such as those in its spectrum (e.g., low frequency and/or tonal components), and, over time, amplitude fluctuations, such as those observed in road, rail, or aircraft noise passages. The larger these fluctuations, the more annoying a sound is generally perceived. Many algorithms have been implemented to quantify these fluctuations and identify noise events, either by looking at transients in the sound level time history, such as exceedances above a fixed or time adaptive threshold, or focusing on the hearing perception process of such events. In this paper, four criteria to detect sound were applied to the acoustic monitoring data collected in two urban areas, namely Andorra la Vella, Principality of Andorra, and Milan, Italy. At each site, the 1 s A-weighted short LAeq,1s time history, 10 min long, was available for each hour from 8:00 a.m. to 7:00 p.m. The resulting 92-time histories cover a reasonable range of urban environmental noise time patterns. The considered criteria to detect noise events are based on: (i) noise levels exceeding by +3 dB the continuous equivalent level LAeqT referred to the measurement time (T), criteria used in the definition of the Intermittency Ratio (IR) to detect noise events; (ii) noise levels exceeding by +3 dB the running continuous equivalent noise level; (iii) noise levels exceeding by +10 dB the 50th noise level percentile; (iv) progressive positive increments of noise levels greater than 10 dB from the event start time. Algorithms (iii) and (iv) appear suitable for notice-event detection; that is, those that (for their features) are clearly perceived and potentially annoy exposed people. The noise events detected by the above four algorithms were also evaluated by the available anomalous noise event detection (ANED) procedure to classify them as produced by road traffic noise or something else. Moreover, the assessment of the sonic environment by the Harmonica index was correlated with the single event level (SEL) of each event detected by the four algorithms. The threshold value of 8 for the Harmonica index, separating the “noisy” from the “very noisy” environments, corresponds to lower SEL levels for notice-events as identified by (iii) and (iv) algorithms (about 88–89 dB(A)) against those identified by (i) and (ii) criteria (92 dB(A)).

Assessment of Road Noise Pollution in Urban Residential Areas—A Case Study in Piteşti, Romania

“…However, recent research [7][8][9][10] has proposed new indicators and algorithms to assess noise disturbance. They consider not only sound energy, but also noise variations, such as the Intermittency Ratio (IR) indicator, or other noise features, such as those in its spectrumthe Harmonica index and its applications.…”

A model study of low-frequency noise exposure indoors due to road traffic

“…That the pass-by sounds have a large possibility to be audible is further substantiated by the coherence with recent work where detection of events assumes that they are 3 dB above the equivalent level. 7,20…”

“…That the pass-by sounds have a large possibility to be audible is further substantiated by the coherence with recent work where detection of events assumes that they are 3 dB above the equivalent level. 7,20 The two curves that at 160 and 200 Hz show an indoor maximum level clearly above the Public Health Agency's curve are calculated for a normal window, with ordinary but not poor sound insulation. This window can thus provide audible passages even at the upper part of the low-frequency range.…”

Managing Circadian Disruption due to Hospitalization: A Pilot Randomized Controlled Trial of the CircadianCare Inpatient Management System

“…The time history of the LAeq was aggregated in 15-min intervals to fit the resolution of the actigraph used for patient rest/activity monitoring. The equivalent background noise level (LA95eq), evaluated every second by the noise level exceeded 95% of the time during the previous T /6 interval, where T is the measurement time, was estimated according to Alsina-Pagès et al (2021). The intermittency ratio (IR) was computed as in Wunderli et al (2016).…”