头相关传递函数的幅度校正与时间对齐插值
Magnitude-Corrected and Time-Aligned Interpolation of Head-Related Transfer Functions
AI总结：
本文提出一种基于时间对齐的HRTF插值后幅度校正方法，利用频率平滑表示显著降低了对侧区域和高频范围的插值误差，改善了双耳电平差异，有望减少实现感知透明插值所需的HRTFs最小数量。
摘要：
头相关传递函数（HRTFs）对于虚拟声学现实至关重要，因为它们包含了在三维空间中定位声源的所有线索。声学测量是获取高质量HRTFs的一种方法。为了减少测量时间、成本以及测量系统的复杂性，一种有前景的方法是仅在稀疏采样网格上捕获少量HRTFs，然后通过插值将其上采样为密集的HRTF集合。然而，HRTF插值具有挑战性，因为声源位置的微小变化可能导致HRTF相位和幅度响应的显著变化。以往的研究通过在预处理中对HRTFs进行时间对齐，极大地改善了插值效果，但幅度插值误差，尤其是在对侧区域，仍然是一个问题。在时间对齐方法的基础上，我们提出了一种额外的插值后幅度校正方法，该方法源自频率平滑的HRTF表示。采用HUTUBS数据库中全部96个个体模拟HRTF集合，我们表明，与仅应用时间对齐的最先进插值方法相比，幅度校正显著降低了插值误差。我们的分析表明，当对非常稀疏的HRTF集合进行上采样时，在关键的高频范围内，所有方向平均的受试者平均幅度误差降低了高达1.5 dB，在对侧区域甚至降低了高达4 dB。因此，上采样HRTFs中的双耳电平差异得到了显著改善。因此，所提出的算法有潜力进一步减少实现感知透明插值所需的HRTFs最小数量。
英文摘要：
Head-related transfer functions (HRTFs) are essential for virtual acoustic realities, as they contain all cues for localizing sound sources in three-dimensional space. Acoustic measurements are one way to obtain high-quality HRTFs. To reduce measurement time, cost, and complexity of measurement systems, a promising approach is to capture only a few HRTFs on a sparse sampling grid and then upsample them to a dense HRTF set by interpolation. However, HRTF interpolation is challenging because small changes in source position can result in significant changes in the HRTF phase and magnitude response. Previous studies greatly improved the interpolation by time-aligning the HRTFs in preprocessing, but magnitude interpolation errors, especially in contralateral regions, remain a problem. Building upon the time-alignment approaches, we propose an additional post-interpolation magnitude correction derived from a frequency-smoothed HRTF representation. Employing all 96 individual simulated HRTF sets of the HUTUBS database, we show that the magnitude correction significantly reduces interpolation errors compared to state-of-the-art interpolation methods applying only time alignment. Our analysis shows that when upsampling very sparse HRTF sets, the subject-averaged magnitude error in the critical higher frequency range is up to 1.5 dB lower when averaged over all directions and even up to 4 dB lower in the contralateral region. As a result, the interaural level differences in the upsampled HRTFs are considerably improved. The proposed algorithm thus has the potential to further reduce the minimum number of HRTFs required for perceptually transparent interpolation.
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