Abstract
One of the main aspects affecting the life of people living in urban and suburban areas is their continued exposure to high road traffic noise (RTN) levels, traditionally measured by specialists working on the field. Nowadays, the deployment of Wireless Acoustic Sensor Networks (WASN) has allowed to automate noise mapping in Smart Cities. In order to obtain a reliable picture of the RTN levels affecting citizens, those anomalous noise events (ANE) unrelated to road traffic should be removed from the noise map computation. For this purpose, an Anomalous Noise Event Detector (ANED) designed to differentiate in real-time between RTN and ANE should be developed to run on the low-cost acoustic sensors of the WASN. In this work, the viability of implementing the ANED algorithm to run on low-capacity (LowCap) μ controller-based acoustic sensors developed within the DYNAMAP project is presented, after being designed and implemented for the high-capacity sensors. The algorithm is based on the comparison between RTN and ANE spectral differences using real-life acoustic data from both suburban and urban scenarios. The results show significant spectral differences between RTN and ANE classes in both environments, after being parametrized using Gammatone Cepstral Coefficients. However, further research should be conducted to determine the most discriminant subbands, which should be taken into account for the implementation of the ANED LowCap version.
Highlights
Living with continuous exposure to high levels of traffic noise has been proved to be harmful for health, affecting the quality of life of people living in urban and suburban areas [1]
In order to remove this kind of events from the road traffic noise (RTN) measurement of the Wireless Acoustic Sensor Networks (WASN), the Anomalous Noise Event Detector (ANED) algorithm has been designed as a two-class classifier (ANE vs. RTN) [9], whose outputs are obtained following a two-level decision process; the frame-level binary decisions are integrated through a high-level decision based on a majority voting scheme computed in a certain interval
It seems viable to take into account these differences in order to implement the ANED LowCap version for low-cost low-capacity acoustic sensors
Summary
Living with continuous exposure to high levels of traffic noise has been proved to be harmful for health, affecting the quality of life of people living in urban and suburban areas [1]. The noise maps are generated from these noise level measurements by means of the application of complex acoustic models after data post-processing; maps that should be updated and published every five years to fulfill the END requirements [2]. This approach becomes difficult to scale when more measurements and/or locations are needed. In order to remove this kind of events from the RTN measurement of the WASN, the ANED algorithm has been designed as a two-class classifier (ANE vs RTN) [9], whose outputs are obtained following a two-level decision process; the frame-level binary decisions are integrated through a high-level decision based on a majority voting scheme computed in a certain interval.
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