Signal Processing Implementation and Comparison of Automotive Spatial Sound Rendering Strategies

Abstract

Design and implementation strategies of spatial sound rendering are investigated in this paper for automotive scenarios. Six design methods are implemented for various rendering modes with different number of passengers. Specifically, the downmixing algorithms aimed at balancing the front and back reproductions are developed for the 5.1-channel input. Other five algorithms based on inverse filtering are implemented in two approaches. The first approach utilizes binaural (Head-Related Transfer Functions HRTFs) measured in the car interior, whereas the second approach named the point-receiver model targets a point receiver positioned at the center of the passenger's head. The proposed processing algorithms were compared via objective and subjective experiments under various listening conditions. Test data were processed by the multivariate analysis of variance (MANOVA) method and the least significant difference (Fisher's LSD) method as a post hoc test to justify the statistical significance of the experimental data. The results indicate that inverse filtering algorithms are preferred for the single passenger mode. For the multipassenger mode, however, downmixing algorithms generally outperformed the other processing techniques.