Auralization model for the perceptual evaluation of tyre–road noise
Journal article, 2017
© 2017 Elsevier Ltd Due to improvements in combustion-engines and use of electric-engines for cars, tyre noise has become the prominent noise source also at lower speeds. Models exist that simulate the noise produced by a rolling tyre, as do models that auralize different traffic situations from basic data. In this paper, a novel auralization method is introduced, with the purpose to enable synthesis of useful car pass-by sound signals for various situations. The method is based on an established model for tyre noise levels (SPERoN) that is combined with a validated auralization tool (LISTEN). In the LISTEN approach, source signals for tyre–road interaction and propulsion are produced from data based on recorded pass-by sounds. In the combined model, the tyre–road interaction data is shaped by the spectra estimated in SPERoN and synthesized back into a pass-by signal. The combined model is made to agree spectrally with measurements for a receiver at 7.5 m distance. Psychoacoustic judgments were used to compare the modelled signals with recorded signals, and the pass-by sounds for a given listener position showed promising quality and accuracy with respect to perceived pleasantness.