一种局部线性嵌入的空间听觉重建方法

Reconstruction of Spatial Hearing Based on Locally Linear Embedding

空间听觉重建中,头相关传输函数(head-related transfer function,HRTF)庞大的数据量是影响虚拟声源合成效率的主要因素之一.为了减少HRTF的数据存储,提出一种局部线性嵌入(locally linear embedding,LLE)空间听觉重建方法.通过LLE对高维HRTF数据进行降维,在低维数据空间提取与方位感知相关的特征,然后利用聚类算法进行分类,得到特征HRTF,而其余非特征HRTF则可以利用特征HRTF通过改进插值算法进行重构.与现有的主成分分析法(principal component analysis,PCA)相比,利用LLE降维后的数据保留了更多的感知信息,利用HRTF数据间的内在关系,对插值后的数据进行修正,可减少重建误差.仿真结果表明,该方法能够有效地减少HRTF的存储数据量,有利于提高虚拟声源的合成效率.

In spatial hearing,mass data of head-related transfer function（HRTF） is a factor that greatly influences the synthesis of virtual sounds.To reduce the data used,we propose a spatial hearing reconstruction method based on locally linear embedding（LLE）.Using LLE,high dimensionality is mapped to a lower dimensional dataset suitable for regressive analysis and classification.To classify by an unsupervised cluster method,a representative HRTF is extracted from all HRTFs.Other HRTFs can be reconstructed in spatial hearing from the representative HRTF with modified interpolation.Compared to the principal component analysis（PCA）,the data with reduced dimension obtained by using LLE preserve more perceptive information.Relations among the HRTF data can be found,with which modified interpolation can be obtained and HRTF reconstruction error can be reduced.Simulation results show that the proposed method effectively reduces HRTF data.It is useful to improve efficiency of synthesis for virtual sound source in practical applications.