AURALIZATION, PERCEPTION AND DETECTION OF TYRE–ROAD NOISE

Abstract: Due to improvements in combustion engines and electric engines for cars, tyre noise has become the prominent noise source at low and medium speeds. Models exist that simulate the noise produced by a rolling tyre, as do models that auralize specific traffic situations from a basic data set. A model that combines both could assist in the planning stage of a tyre by delivering not only estimates of the physical behaviour of the tyre, but also by further making the resulting sound perceivable. Further, such a model could help to design acoustic traffic situations with full control of all parameters. Focusing on that, this thesis has three aims. All focus is on the perception of the sound of a car from the outside, perceived by, for example, a pedestrian. The first aim is to combine an established model for tyre noise (SPERoN) with an auralization tool. The combined model can predict the spectrum of a car pass-by at 7.5 m, as well as reproduce the sound at a given listener position. Psychoacoustic judgements are used to compare the modelled signals with recorded signals. It was found that responses for simulated and recorded signals correlated for all cases, but the ranked orders differed slightly. The second aim focuses on the perception of tyre–road noise and whether it can be differentiated and characterized by its perceptual qualities. When designing tyre sounds, the main aim should be to increase the pleasantness of the total vehicle sound while maintaining the carried information and reducing the sound level. Achieving this requires an understanding of how physical changes in a tyre are reflected in the perception of that tyre. Listeners were asked to judge different tyre–road combinations and their perception in terms of their emotional and psychoacoustic responses. The results confirmed that rolling noise can be perceptually differentiated. The third aim in this thesis was to increase understanding of the parameters that influence the detection of a single car in background traffic noise. For this, both variations in the sound of the test car and in the background (e.g. distance, traffic amount, speed, tyre/engine noise) were investigated and found to influence the reaction time. The introduced auralization method was utilized to generate the sound files for the different traffic situations.