基于 RBF 神经网络的头相关传输函数的个性化建模方法

doi:10.3969/j.issn.1007-2861.2013.07.019

上海大学学报(自然科学版) ›› 2014, Vol. 20 ›› Issue (2): 157-164.doi: 10.3969/j.issn.1007-2861.2013.07.019

基于 RBF 神经网络的头相关传输函数的个性化建模方法

黄青华, 李琳, 赖士村

上海大学通信与信息工程学院, 上海 200444

出版日期:2014-04-26 发布日期:2014-04-26
通讯作者: 黄青华(1978—), 女, 副研究员, 博士, 研究方向为3D音频信号处理和盲信号处理.E-mail: qinghua@shu.edu.cn
作者简介:黄青华(1978—), 女, 副研究员, 博士, 研究方向为3D音频信号处理和盲信号处理.E-mail: qinghua@shu.edu.cn
基金资助:
国家自然科学基金资助项目(61001160); 上海市教委创新基金资助项目(12YZ023)

Personalized Modeling of Head-Related Transfer Function Based on RBF Neural Network

HUANG Qing-hua, LI Lin, LAI Shi-cun

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Online:2014-04-26 Published:2014-04-26

摘要/Abstract

摘要： 采用主成分分析方法提取头相关传输函数 (head-ralated transfer function, HRTF) 的个性化系数, 计算了影响HRTF 的人体参数的拉普拉斯得分, 并联合 Pearson 相关系数提取出对HRTF 影响显著的关键人体参数; 构建了径向基函数 (radial basis function, RBF) 神经网络, 学习关键人体参数到头相关传输函数个性化系数的非线性映射模型, 利用简单的人体参数测量估计出待测者的个性化头相关传输函数. 通过实验仿真与偏最小二乘回归 (partial least squares regression, PLSR) 法比较可知, RBF 神经网络个性化学习方法的性能优于PLSR 法.

关键词: RBF神经网络, 个性化, 拉普拉斯得分, 头相关传输函数

Abstract: A nonlinear personalized model is established to predict individual head-related transfer function (HRTF). Principal component analysis (PCA) is applied to obtain individual weights as the outputs of the model. Some important anthropometric parameters are selected according to the Laplacian score and correlation analyses between all measured parameters and the individual weights. They act as the inputs to the model. Constructing a radial basis function (RBF) neural network to learn the nonlinear model, the individual HRTF can be predicted according to the measured anthropometric parameters. Simulation results show that the proposed method outperforms the partial least squares regression (PLSR) method in predicting individual HRTF.

Key words: head-related transfer function, Laplacian score, personalization, radial basis function neural network

中图分类号:

TB 52
TP 183

黄青华, 李琳, 赖士村. 基于 RBF 神经网络的头相关传输函数的个性化建模方法[J]. 上海大学学报(自然科学版), 2014, 20(2): 157-164.

HUANG Qing-hua, LI Lin, LAI Shi-cun. Personalized Modeling of Head-Related Transfer Function Based on RBF Neural Network[J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(2): 157-164.

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基于 RBF 神经网络的头相关传输函数的个性化建模方法

Personalized Modeling of Head-Related Transfer Function Based on RBF Neural Network

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