Personalised electric vehicle acoustics with generative AI: Dynamic sonification and user acceptance study

Verstelle, W., Alam, M. S., Bazilinskyy, P.

Submitted for publication.

ABSTRACT Electric vehicles (EVs) reduce powertrain noise, creating safety challenges and opportunities for sound design. This paper examines whether generative audio supports personalised dynamic acoustics for future EVs. We report a research through design study in which AI-generated sound samples were selected, edited into seamless loops and embedded in a sonification (the process of translating non-auditory data into sound) prototype. The prototype connects a Processing vehicle interface to a Pure Data audio engine using Open Sound Control. Vehicle speed and throttle input modulate pitch, amplitude and load related parameters. A user study with 20 participants combined the Acceptance Scale with open questions. The results show moderately positive acceptance, with an average usefulness of 0.88 and a satisfaction of 0.62 on a scale from -2 to +2. Participants valued personalisation and responsiveness, but requested stronger recognisability and better throttle mapping. Generative AI is useful for early EV acoustic prototyping, while the final design requires expert refinement and safety evaluation.