Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology and are essential for listeners to localize sound sources in virtual auditory displays. Since rendering complex virtual scenes is computationally demanding, we propose four algorithms for efficiently representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. All four algorithms use sparse approximation procedures to minimize the computational complexity while maintaining perceptually relevant HRTF properties. The first two algorithms separately optimize the complexity of the transfer matrix associated to each HRTF for fixed FBs. The other two algorithms jointly optimize the FBs and transfer matrices for complete HRTF sets by two variants. The first variant aims at minimizing the complexity of the transfer matrices, while the second one does it for the FBs. Numerical experiments investigate the latency-complexity trade-off and show that the proposed methods offer significant computational savings when compared with other available approaches. Psychoacoustic localization experiments were modeled and conducted to find a reasonable approximation tolerance so that no significant localization performance degradation was introduced by the subband representation.

中文翻译：

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子带中与头部相关的传递函数的有效近似，以实现准确的声音定位。


头部相关传递函数 (HRTF) 描述了人体形态对传入声音的声学过滤，对于听众在虚拟听觉显示中定位声源至关重要。由于渲染复杂的虚拟场景在计算上要求很高，因此我们提出了四种有效表示子带中 HRTF 的算法，即作为分析滤波器组 (FB)，后跟传输矩阵和合成 FB。所有四种算法都使用稀疏近似程序来最小化计算复杂性，同时保持感知相关的 HRTF 属性。前两种算法分别优化与固定 FB 的每个 HRTF 相关的传输矩阵的复杂性。另外两种算法通过两种变体联合优化完整 HRTF 集的 FB 和传输矩阵。第一个变体旨在最小化传输矩阵的复杂性，而第二个变体则针对 FB。数值实验研究了延迟与复杂性的权衡，并表明与其他可用方法相比，所提出的方法可显着节省计算量。对心理声学定位实验进行建模并进行，以找到合理的近似容差，以便子带表示不会导致明显的定位性能下降。