Vehicle driving simulators allow to recreate the experience of driving a real vehicle. The implementation of such simulators requires the use of sound reproduction methods, which may influence the sound perception differently inside the vehicles. This study investigated sound localization in a virtual-reality-based driving simulator for three sound reproduction methods: higher-order Ambisonics (HOA) reproduced with a spherical array of 64 loudspeakers, the matrix inversion method reproduced with a circular array including eight loudspeakers and the binaural reproduction with headphones. Ten experienced drivers provided sound localization judgments in terms of perceived distance and horizontal angle. Sound recordings were made inside a real vehicle and presented at five source positions inside the car in the virtual sound environment. The simulated scenarios included the operating conditions of idling and constant speed at 100 km/h, as well as a warning and a speech signal. The binaural reproduction over headphones outperformed the reproduction methods with loudspeakers in terms of perceived horizontal angle, whereas perceived distance was more accurate for the methods including loudspeakers. Moreover, distance perception was more affected for the 100 k/m than for the idling condition. The results highlight the need to consider the sound reproduction method when developing and testing vehicle features.


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    Titel :

    Sound reproduction methods for source localization in virtual-reality-based automotive simulators.


    Beteiligte:

    Erscheinungsdatum :

    2022-01-01


    Anmerkungen:

    Jo , E S , Park , D C , Gil Carvajal , J C , Jeong , C-H & Song , W 2022 , Sound reproduction methods for source localization in virtual-reality-based automotive simulators. in Proceedings of the 24 th International Congress on Acoustics . 24 th International Congress on Acoustics , Gyeongju , Korea, Republic of , 24/10/2022 .


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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