HRTFformer: 用于沉浸式音频渲染中个体HRTF上采样的空间感知Transformer
HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering
AI总结：
针对个体HRTF测量困难的问题，提出基于Transformer的HRTF上采样架构，利用注意力机制和球谐域处理，结合邻域差异损失，实现高保真HRTF重建。
摘要：
个体头相关传输函数（HRTF）正开始被引入许多商业沉浸式音频应用中，对于实现逼真的空间音频渲染至关重要。然而，引入它们的主要顾虑之一是，由于HRTF测量过程的复杂性，大规模创建个体HRTF并不实用。为缓解这一缺点，提出了HRTF空间上采样，旨在减少所需的测量量。尽管先前的工作已通过不同的机器学习方法取得成功，但这些模型通常难以在相邻源方向之间保持局部空间变化模式的长期一致性，以及在高上采样因子下的泛化能力。本文提出了一种新颖的基于Transformer的HRTF上采样架构，利用注意力机制更好地捕捉HRTF球面上的空间相关性。在球谐域中工作，我们的模型从稀疏输入测量中学习重建高分辨率HRTF，精度显著提高。为增强空间一致性，我们引入了邻域差异损失，促进幅度平滑性，从而产生更逼真的上采样。我们使用感知定位模型和客观频谱失真指标评估了我们的方法。实验表明，我们的模型在生成逼真、高保真HRTF方面，在多个评估指标上优于现有方法。
英文摘要：
Individual Head-Related Transfer Functions (HRTFs) are starting to be introduced in many commercial immersive audio applications and are crucial for realistic spatial audio rendering. However, one of the main hesitations regarding their introduction is that creating individual HRTFs is impractical at scale due to the complexities of the HRTF measurement process. To mitigate this drawback, HRTF spatial upsampling has been proposed with the aim of reducing the measurements required. While prior work has seen success with different machine learning (ML) approaches, these models often struggle with long-range preservation of local spatial variation patterns across neighbouring source directions and generalization at high upsampling factors. In this paper, we propose a novel transformer-based architecture for HRTF upsampling, leveraging the attention mechanism to better capture spatial correlations across the HRTF sphere. Working in the spherical harmonic (SH) domain, our model learns to reconstruct high-resolution HRTFs from sparse input measurements with significantly improved accuracy. To enhance spatial coherence, we introduce a neighbour dissimilarity loss that promotes magnitude smoothness, yielding more realistic upsampling. We evaluate our method using both perceptual localization models and objective spectral distortion metrics. Experiments show that our model outperforms existing methods across several evaluation metrics in generating realistic, high-fidelity HRTFs.
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