用于HRTF个性化的空间相关性增强图神经场
Graph Neural Field with Spatial-Correlation Augmentation for HRTF Personalization
AI总结：
针对VR/AR设备中HRTF测量耗时且依赖受试者的问题，提出GraphNF-SCA模型，通过三个模块利用空间相关性生成未见过受试者的个性化HRTFs，提升HRTF个性化性能并达到最先进效果。
摘要：
为在VR/AR设备上实现沉浸式空间音频渲染，高质量的头相关传输函数（HRTFs）至关重要。通常，HRTFs具有受试者依赖性和位置依赖性，其测量耗时且繁琐。为应对这一挑战，我们提出空间相关性增强图神经场（GraphNF-SCA）用于HRTF个性化，可对未见过的受试者生成个体HRTFs。GraphNF-SCA包含三个关键组件：HRTF个性化（HRTF-P）模块、HRTF上采样（HRTF-U）模块和微调阶段。HRTF-P模块采用编码器-解码器架构的图神经网络（GNN）预测目标受试者的HRTFs，编码器提取通用特征，解码器整合目标相关特征并生成个性化HRTFs；HRTF-U模块采用另一GNN对HRTFs的空间相关性建模，利用HRTF-P模块的输出进行微调以增强预测HRTFs的空间一致性。与现有逐位置估计个体HRTFs且未建模空间相关性的方法不同，GraphNF-SCA有效利用HRTFs间的固有空间相关性提升HRTF个性化性能。实验结果表明，GraphNF-SCA达到了最先进的效果。
英文摘要：
To achieve immersive spatial audio rendering on VR/AR devices, high-quality Head-Related Transfer Functions (HRTFs) are essential. In general, HRTFs are subject-dependent and position-dependent, and their measurement is time-consuming and tedious. To address this challenge, we propose the Graph Neural Field with Spatial-Correlation Augmentation (GraphNF-SCA) for HRTF personalization, which can be used to generate individual HRTFs for unseen subjects. The GraphNF-SCA consists of three key components: an HRTF personalization (HRTF-P) module, an HRTF upsampling (HRTF-U) module, and a fine-tuning stage. In the HRTF-P module, we predict HRTFs of the target subject via the Graph Neural Network (GNN) with an encoder-decoder architecture, where the encoder extracts universal features and the decoder incorporates the target-relevant features and produces individualized HRTFs. The HRTF-U module employs another GNN to model spatial correlations across HRTFs. This module is fine-tuned using the output of the HRTF-P module, thereby enhancing the spatial consistency of the predicted HRTFs. Unlike existing methods that estimate individual HRTFs position-by-position without spatial correlation modeling, the GraphNF-SCA effectively leverages inherent spatial correlations across HRTFs to enhance the performance of HRTF personalization. Experimental results demonstrate that the GraphNF-SCA achieves state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
