论文标题
数据集的增强和降低PINNA相关传输功能的尺寸
Dataset Augmentation and Dimensionality Reduction of Pinna-Related Transfer Functions
论文作者
论文摘要
头部相关转移功能（HRTF）的个体间变化的有效建模是双耳合成个性化的关键问题。在以前的工作中，我们增强了119对耳朵形状和与Pinna相关的传递功能（PRTFS）的数据集，从而创建了一个宽的数据集，该数据集由1005个耳朵形状和通过随机耳朵图（广泛）和声学模拟的prTFSGENER。在本文中，我们分别研究了两个主要成分分析（PCA）模型PRTF，Trainedon广泛和原始数据集的降低能力。我们发现，无论保留的主组件的数量如何，经过广泛培训的模型都表现最好。
Efficient modeling of the inter-individual variations of head-related transfer functions (HRTFs) is a key matterto the individualization of binaural synthesis. In previous work, we augmented a dataset of 119 pairs of earshapes and pinna-related transfer functions (PRTFs), thus creating a wide dataset of 1005 ear shapes and PRTFsgenerated by random ear drawings (WiDESPREaD) and acoustical simulations. In this article, we investigate thedimensionality reduction capacity of two principal component analysis (PCA) models of magnitude PRTFs, trainedon WiDESPREaD and on the original dataset, respectively. We find that the model trained on the WiDESPREaDdataset performs best, regardless of the number of retained principal components.
