Automating the assessment of sound power levels of running vehicles using information extracted from a static video

Abstract

Noise has become a ubiquitous pollutant in big cities, especially Road Traffic Noise compared with other means of transportation. Several countries/regions have developed road noise prediction models based on local measurements, adjusting their requirements and goals for evaluating this pollutant. Differently than most developed countries, industrializing countries have different characteristics of noise generation due to implied differences, such as degree of maintenance (both vehicle and road conditions) and the driving behavior, for instance. The data acquisition needed for evaluating the vehicle fleet's sound power emission is expensive and time-consuming. This research proposes using a video camera close to the microphone to automate the data gathering and analysis. Using a Python script, this system extracts the sound pressure, estimates the running speed, assesses the distance from the receiver point, and classifies the vehicle under investigation. In addition, to discard inaccurate data, the expected trajectory and sound pressure are evaluated. The proposed system's performance was compared against manual measurements. The resulting sound levels differ less than 0.2 dB for most cases. Thus, acquired 3.4 times the amount of data in the same time interval. After this verification, measurements in different conditions and vehicle fleet were tested in Hokkaido, Oita, and São Paulo cities.