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.
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.
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