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Psychoacoustics parameters are widely employed in automotive field for objective evaluation of Sound Quality (SQ) of vehicle cabins and their components. The standard approach relies on binaural recordings from which numerical values and curves are calculated. In addition, head-locked binaural listening playback can be performed. The Virtual Reality (VR) technology recently started to diffuse also in automotive field, bringing new possibilities for enhanced and immersive listening sessions, thanks to the usage of massive microphone arrays instead of binaural microphones. In this paper, we combine both solutions: the principal SQ parameters are derived from multichannel recordings. This allows computing a map of direction-dependent values of SQ parameters. The acquisition system consists in a spherical microphone array with 32 capsules and a multiple-lens camera for capturing a panoramic equirectangular background image.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.