Audio Communication Group, Technical University of Berlin, Einsteinufer 17c, D-10587, Germany.
Huawei Technologies, Munich Research Centre, Riesstrasse 25, D-80992 Munich, Germany.
J Acoust Soc Am. 2020 Dec;148(6):3809. doi: 10.1121/10.0002884.
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.
头相关传递函数 (HRTF) 的个性化可以提高双耳应用的质量，在定位准确性、染色和其他方面。使用头部、颈部和耳廓的人体测量特征 (AFs) 进行个性化是一种避免繁琐的声学测量或数值模拟的有前途的方法。以前关于 HRTF 个性化的研究分析了 AFs 与技术 HRTF 特征之间的联系。然而，特定误差的感知相关性并不总是清楚的。因此，直接探讨了 AFs 对整体差异、染色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，其中受试者对自己的 HRTF 和一组非个性化 HRTF 之间的差异进行了评分。基于这些数据，开发了一种机器学习模型，使用受试者的个体 AF 与呈现的非个体 AF 的比值来预测感知差异。结果表明，感知差异可以很好地预测，并且模型推荐的 HRTF 比通用或随机选择的 HRTF 提供了明显的改进。此外，还确定了每种类型误差预测的最相关 AF。开发的模型可根据免费文化许可证使用。
