Automotive sound field reproduction using deep optimization with spatial domain constraint

Sound field reproduction with undistorted sound quality and precise spatial localization is desirable for automotive audio systems. However, the complexity of the automotive cabin acoustic environment often necessitates a trade-off between sound quality and spatial accuracy. To overcome this limitation, we propose Spatial Power Map Net, a learning-based sound field reproduction method that improves both sound quality and spatial localization in complex environments. We introduce a spatial power map constraint, which characterizes the angular energy distribution of the reproduced field using beamforming. This constraint guides energy toward the intended direction to enhance spatial localization, and is integrated into a multi-channel equalization framework to also improve sound quality under reverberant conditions. To address the resulting non-convexity, deep optimization that uses neural networks to solve optimization problems is employed for filter design. Both in situ objective and subjective evaluations confirm that our method enhances sound quality and improves spatial localization within the automotive cabin. Furthermore, we analyze the influence of different audio materials and the arrival angles of the virtual sound source in the reproduced sound field, investigating the potential underlying factors affecting these results.