Automated identification and assessment of environmental noise sources

Abstract

Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.

Keywords: Automatization of measurements; Environmental noise; Immission directivity; Microphone array; Noise source classification; Spatial filtering.

Graphical abstract

Fig. 1

Sound pressure levels from multiple sources. Measurements with a single microphone can only record the combined sound pressure level.

Fig. 2

a) Microphone array in anechoic chamber, b) Prototype paired with calibrated SLM for noise measurements.

Fig. 3

DOA detection with DSS algorithm: a) Plane sound waves are detected by a microphone array, b) ASDF is calculated for each pair of microphones, c) Beam pattern of the detected noise source is generated.

Fig. 4

Three different scenarios with three different noise sources with different parameters and contributions to the total noise level at the immission point.

Fig. 5

a) Sound pressure level and the detected direction of the dominant noise source plotted against time, b) Three different examples of source dominance $\Theta$ behaviour.

Fig. 6

a) Location of the measurement points in Ljubljana, b) Immission directivity for the measurement point near the railroad and the warehouse (MP1), c) Immission directivity at the parking lot of the Faculty of Mechanical Engineering (MP2), d) Immission directivity in the train station Moste (MP3), [128].

Fig. 7

Recorded data of L_p,A, direction and source dominance Θ from MP1 of: a) The entire measurement, b) Train pass-by. Classified data points from MP1 plotted against: c) Time and L_p,A, d) Direction and L_p,A (together with immission directivity), d) direction by their total number in each class.

Fig. 8

Recorded data of $L_{p,A}$, direction and source dominance $\Theta$ from MP2 of: a) The entire measurement, b) Train pass-by. Classified datapoints from MP2 plotted against: c) Time and $L_{p,A}$, d) Direction and $L_{p,A}$ (together with immission directivity), d) direction by their total number in each class.

Fig. 9

Recorded data of $L_{p,A}$, direction and source dominance $\Theta$ from MP3 of: a) The entire measurement, b) Train pass-by. Classified data points from MP3 plotted against: c) Time and $L_{p,A}$, d) Direction and $L_{p,A}$ (together with immission directivity), d) direction by their total number in each class.