摘要
为了构建关于仰角方向连续的头相关传输函数(HRTF),提出采用径向基函数神经网络外推中垂面上低仰角方向HRTF的方法.基于KEMAR人工头和真人的数据,对3种不同网络输入方式下外推方法的有效性进行了研究.结果表明:外推值和测量值的相关系数可达0.93;增大网络输入的数据量、减小外推方向和已知方向的空间距离可提高外推的准确性;外推的误差随频率的升高而增大.
In order to construct a continuous head-related transfer function （HRTF） along elevation, a method to extrapolate the HRTF at low elevation of the median plane is proposed based on the radial basis function （RBF） neural network. Then, according to the data of KEMAR mannequin and a human being, the performances of the proposed method in three network input methods are analyzed. The results show that the correlative coefficient between the extrapolated and the measured HRTFs is as high as 0.93, that the extrapolation accuracy can be improved by increasing the data of network input and by reducing the spatial distance between the extrapolated and the known directions, and that the extrapolation error increases with frequency.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第9期20-25,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(10374031)
关键词
头相关传输函数
外推
神经网络
虚拟现实
head-related transfer function
extrapolation
neural network
virtual reality
