A WASN-Based Suburban Dataset for Anomalous Noise Event Detection on Dynamic Road-Traffic Noise Mapping

Abstract

Traffic noise is presently considered one of the main pollutants in urban and suburban areas. Several recent technological advances have allowed a step forward in the dynamic computation of road-traffic noise levels by means of a Wireless Acoustic Sensor Network (WASN) through the collection of measurements in real-operation environments. In the framework of the LIFE DYNAMAP project, two WASNs have been deployed in two pilot areas: one in the city of Milan, as an urban environment, and another around the city of Rome in a suburban location. For a correct evaluation of the noise level generated by road infrastructures, all Anomalous Noise Events (ANE) unrelated to regular road-traffic noise (e.g., sirens, horns, speech, etc.) should be removed before updating corresponding noise maps. This work presents the production and analysis of a real-operation environmental audio database collected through the 19-node WASN of a suburban area. A total of 156 h and 20 min of labeled audio data has been obtained differentiating among road-traffic noise and ANEs (classified in 16 subcategories). After delimiting their boundaries manually, the acoustic salience of the ANE samples is automatically computed as a contextual Signal-to-Noise Ratio (SNR) together with its impact on the A-weighted equivalent level ( Δ L A e q ). The analysis of the real-operation WASN-based environmental database is evaluated with these metrics, and we conclude that the 19 locations of the network present substantial differences in the occurrences of the subcategories of ANE, with a clear predominance of the noise of sirens, trains, and thunder.

Characterization of a WASN-Based Urban Acoustic Dataset for the Dynamic Mapping of Road Traffic Noise

“…The acoustic dataset was manually labelled by 5 experts in audio signal processing, who used Audacity software to perform the labelling process with the aid of visual information (waveform and spectrogram in dB) while listening to the recorded signals. The labelling process follows the methodology described in [16], asking the experts to classify each portion of the audio signal according to following criteria: i) those audio clips containing road-traffic noise should be labelled as RTN. They may contain all kinds of sounds coming from vehicle engines and tyres even if they are distant or practically nonexistent, if any other sound prevails; ii) those sounds unrelated to regular RTN, should be labelled as ANEs (several subcategories are identified by the experts during the labelling process); and iii) those audio passages containing a high diversity of sound sources should be labelled as complex sound mixtures (CMPLX).…”

“…Taking advantage of this fact, a new dataset has been built so as to improve the ANED algorithm with respect to the previous implementation based on preliminary manual recordings [14]. As in the WASN-based suburban dataset (see [16] for further details), the WASN-based urban dataset includes data from weekdays and weekends, and environmental noise samples from the definitive location of the sensors in real-operation. In the previous recordings in the District 9 of Milan [17], the audio was recorded using a tripod placed on the street instead of the façades, where the low-cost sensors have been finally placed (see Figure1).…”

BCNDataset: Description and Analysis of an Annotated Night Urban Leisure Sound Dataset

“…It is worth mentioning that the recordings in the urban area were conducted at the street level at pre-selected locations within District 9 of Milan [28], while the recordings in the suburban area were conducted on the A90 ring-road portals surrounding Rome (see [29] for further details). In the final stage of the LIFE+ DYNAMAP project, in [30], the same authors presented the production and analysis of a real-operation environmental audio database collected through the 19-node WASN of a suburban area of Rome. As a result, 156 h and 20 min of labeled audio data were obtained, differentiating among RTN and ANEs (classified in 16 subcategories).…”

Preliminary Acoustic Analysis of Farm Management Noise and Its Impact on Broiler Welfare

“…The labeling was conducted with the goal of finding the types of events described by the farmer; nevertheless, once a file was being labeled, all the events were labeled with their proper name. The reader is referred to [13] for more details of the data labelling. Figure 1.…”

“…It is computed as calculating the difference (see Equation (3)) of the L eq,event of the segment with the event and the L eq,event of the same audio segment replacing the event with a linear interpolation from the first to the last sample of the original data. Find a more detailed calculation explanation in [13,14].…”