用深度优化提升汽车内声场还原，兼顾音质与方向感。
Automotive sound field reproduction using deep optimization with spatial domain constraint
- 引入空间功率图约束，引导声能聚焦指定方向。
- 在混响环境下实现音质提升与定位精度双重改善。
- 适合车载音频系统开发与声学算法研究者。
追求无失真音质和精准空间定位的声场还原对汽车音频系统至关重要。然而，汽车舱内声学环境复杂，常需在音质与空间准确性之间权衡。为此，我们提出基于学习的声场还原方法SPMnet，通过引入空间功率图（SPM）约束，利用波束成形刻画再现声场的角度能量分布，引导声能向目标方向集中，从而增强空间定位能力；该约束被整合进多通道均衡框架，在混响条件下同时改善音质。针对由此带来的非凸性问题，采用神经网络驱动的深度优化方法求解滤波器设计。现场客观与主观评估均证实，本方法显著提升了车内音质并改善了空间定位性能。此外，我们分析了不同音频内容及虚拟声源入射角对再现效果的影响，探究潜在作用因素。
原文摘要 · Abstract (English)
Sound field reproduction with undistorted sound quality and precise spatial localization is desirable for automotive audio systems. However, the complexity of 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 (SPMnet), a learning-based sound field reproduction method that improves both sound quality and spatial localization in complex environments. We introduce a spatial power map (SPM) 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 use 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.
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