Scaling model for a speed-dependent vehicle noise spectrum

Considering the well-known features of the noise emitted by moving sources, a number of vehicle characteristics such as speed, unladen mass, engine size, year of registration, power and fuel were recorded in a dedicated monitoring campaign performed in three different places, each characterized by different number of lanes and the presence of nearby reflective surfaces. A full database of 144 vehicles (cars) was used to identify statistically relevant features. In order to compare the vehicle transit noise in different environmental conditions, all 1/3-octave band spectra were normalized and analysed. Unsupervised clustering algorithms were employed to group together spectrum levels with similar profiles. Our results corroborate the well-known fact that speed is the most relevant characteristic to discriminate between different vehicle noise spectrum. In keeping with this fact, we present a new approach to predict analytically noise spectra for a given vehicle speed. A set of speed-dependent analytical functions are suggested in order to fit the normalized average spectrum profile at different speeds. This approach can be useful for predicting vehicle speed based purely on its noise spectrum pattern. The present work is complementary to the accurate analysis of noise sources based on the beamforming technique.

• Record URL:
  https://doi.org/10.1016/j.jtte.2017.05.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2095756417301873
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20957564
• Supplemental Notes:
  • © 2017 Periodical Offices of Chang'an University. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zambon, Giovanni
  • Roman, H Eduardo
  • Benocci, Roberto
• Publication Date: 2017-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 230-239
• Serial:
  • Journal of Traffic and Transportation Engineering (English Edition)
  • Volume: 4
  • Issue Number: 3
  • Publisher: Elsevier
  • ISSN: 2095-7564
  • Serial URL: http://www.journals.elsevier.com/journal-of-traffic-and-transportation-engineering-english-edition
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Acoustic emission tests; Automobiles; Cluster analysis; Mathematical models; Mathematical prediction; Sound level; Speed; Speed data; Statistical analysis; Traffic noise
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01645146
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 22 2017 2:25PM