拆解声学建模对个人声区渲染的影响，指导有限预算下训练数据构建。
Decomposing the Influence of Physical Acoustic Modeling on Neural Personal Sound Zone Rendering: An Ablation Study
- 逐项加入扬声器频响、指向性与头相关传递函数，系统评估各成分贡献。
- 指向性模型提升声区分离10.05 dB，是改善隔离效果的关键因素。
- 适用于音频系统设计与神经渲染研究者，尤其关注真实场景泛化性能。
基于深度学习的个人声区（PSZ）系统依赖模拟的声学传递函数（ATFs）进行训练，但理想化的点源模型存在显著的仿真到现实差距。尽管引入物理先验可提升泛化能力，但各组件的具体贡献仍不明确。本文针对一种头姿态相关的双耳PSZ渲染器（BSANN），开展受控消融实验，逐步在模拟ATFs中加入三类物理成分：(i) 实测扬声器频响（FR）、(ii) 解析圆柱活塞指向性（DIR）、(iii) 刚性球头相关传递函数（RS-HRTF）。通过两个假人头的现场测量，评估四种配置在100–20000 Hz范围内的性能，指标包括区间隔离度（IZI）、程序间干扰（IPI）和串扰抑制（XTC）。结果表明：FR实现频谱校准，带来适度的XTC改善和听众间IPI不平衡降低；DIR提供最稳定的声区分离增益（平均IZI/IPI提升10.05 dB）；RS-HRTF主导双耳分离效果，使XTC提升+2.38/+2.89 dB（平均从4.51提升至7.91 dB），主要体现在2 kHz以上频段，但引入轻微听者依赖的IZI/IPI波动。研究为资源受限时训练ATFs的建模优先级提供了明确指导。
原文摘要 · Abstract (English)
Deep learning-based Personal Sound Zones (PSZs) rely on simulated acoustic transfer functions (ATFs) for training, yet idealized point-source models exhibit large sim-to-real gaps. While physically informed components improve generalization, individual contributions remain unclear. This paper presents a controlled ablation study on a head-pose-conditioned binaural PSZ renderer using the Binaural Spatial Audio Neural Network (BSANN). We progressively enrich simulated ATFs with three components: (i) anechoically measured frequency responses of the particular loudspeakers(FR), (ii) analytic circular-piston directivity (DIR), and (iii) rigid-sphere head-related transfer functions (RS-HRTF). Four configurations are evaluated via in-situ measurements with two dummy heads. Performance metrics include inter-zone isolation (IZI), inter-program interference (IPI), and crosstalk cancellation (XTC) over 100-20000 Hz. Results show FR provides spectral calibration, yielding modest XTC improvements and reduced inter-listener IPI imbalance. DIR delivers the most consistent sound-zone separation gains (10.05 dB average IZI/IPI). RS-HRTF dominates binaural separation, boosting XTC by +2.38/+2.89 dB (average 4.51 to 7.91 dB), primarily above 2 kHz, while introducing mild listener-dependent IZI/IPI shifts. These findings guide prioritization of measurements and models when constructing training ATFs under limited budgets.
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