论文标题
由随机耳朵图产生的宽宽耳朵形状数据集和与PINNA相关的传递功能
A Wide Dataset of Ear Shapes and Pinna-Related Transfer Functions Generated by Random Ear Drawings
论文作者
论文摘要
与头部相关的传递函数（HRTF）个性化是双耳合成的关键问题。但是，与数据的高维度相比，目前可用的数据库的大小有限。在此，我们介绍了生成1000个耳朵形状的合成数据集和与PINNA相关传输功能（PRTFS）的匹配集（称为广泛的）（EAR形状的宽数据集和与Pinna相关的传递功能的广泛数据集），并通过随机耳朵图获得），并免费提供给其他研究人员。本文的贡献是三倍。首先，从119个三维左耳扫描的专有数据集中，我们通过执行快速 - 多极子边界元素方法（FM-BEM）计算来构建PRTF的匹配数据集。其次，我们使用主成分分析（PCA）研究了每种高维数据的潜在几何形状。我们发现，这种线性机器学习技术在模拟和降低耳朵形状的数据维度方面的表现要好于与匹配PRTF集合相比。第三，根据这些发现，我们设计了一种方法来生成任意大型的PRTF集合集合数据库，该数据库依赖于耳朵形状的随机绘制和随后的FM-BEM计算。
Head-related transfer functions (HRTFs) individualization is a key matter in binaural synthesis. However, currently available databases are limited in size compared to the high dimensionality of the data. Hereby, we present the process of generating a synthetic dataset of 1000 ear shapes and matching sets of pinna-related transfer functions (PRTFs), named WiDESPREaD (wide dataset of ear shapes and pinna-related transfer functions obtained by random ear drawings) and made freely available to other researchers. Contributions in this article are three-fold. First, from a proprietary dataset of 119 three-dimensional left-ear scans, we build a matching dataset of PRTFs by performing fast-multipole boundary element method (FM-BEM) calculations. Second, we investigate the underlying geometry of each type of high-dimensional data using principal component analysis (PCA). We find that this linear machine learning technique performs better at modeling and reducing data dimensionality on ear shapes than on matching PRTF sets. Third, based on these findings, we devise a method to generate an arbitrarily large synthetic database of PRTF sets that relies on the random drawing of ear shapes and subsequent FM-BEM computations.
