Individualization of head-related transfer functions (HRTFs) can improve the quality of binaural applications with respect to the localization accuracy, coloration, and other aspects. Using anthropometric features (AFs) of the head, neck, and pinna for individualization is a promising approach to avoid elaborate acoustic measurements or numerical simulations. Previous studies on HRTF individualization analyzed the link between AFs and technical HRTF features. However, the perceptual relevance of specific errors might not always be clear. Hence, the effects of AFs on perceived perceptual qualities with respect to the overall difference, coloration, and localization error are directly explored. To this end, a listening test was conducted in which subjects rated differences between their own HRTF and a set of nonindividual HRTFs. Based on these data, a machine learning model was developed to predict the perceived differences using ratios of a subject's individual AFs and those of presented nonindividual AFs. Results show that perceived differences can be predicted well and the HRTFs recommended by the models provide a clear improvement over generic or randomly selected HRTFs. In addition, the most relevant AFs for the prediction of each type of error were determined. The developed models are available under a free cultural license.

中文翻译：

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基于感官相似性和人体测量学特征的基于头部的传递函数推荐

头部相关传递函数（HRTF）的个性化可以提高双耳应用在定位精度，颜色和其他方面的质量。使用头，颈和耳廓的人体测量特征（AF）进行个性化是避免复杂的声学测量或数值模拟的有前途的方法。先前有关HRTF个性化的研究分析了AF与HRTF技术功能之间的联系。但是，特定错误的感知相关性可能并不总是很清楚。因此，直接探讨了自动对焦对整体差异，色彩和定位误差的感知感知质量的影响。为此，进行了一项听力测试，受试者对他们自己的HRTF与一组非个人HRTF之间的差异进行了评分。根据这些数据，开发了机器学习模型，以使用受试者的个体房颤与呈现的非个体房颤的比率预测感知到的差异。结果表明，可以很好地预测感知到的差异，并且模型推荐的HRTF与普通或随机选择的HRTF相比有明显的改进。此外，确定了与每种错误类型的预测最相关的AF。所开发的模型可以免费获得文化许可。确定与每种错误类型最相关的AF。所开发的模型可以免费获得文化许可。确定与每种错误类型最相关的AF。所开发的模型可以免费获得文化许可。