Modeling personalized head-related impulse response using support vector regression

A new customization approach based on support vector regression (SVR) is proposed to obtain individual headrelated
impulse response (HRIR) without complex measurement and special equipment. Principal component analysis (PCA) is
first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric
parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric
parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight
vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the
measured anthropometric parameters. Compared with a back-propagation neural network (BPNN) for nonlinear regression,
better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR
algorithm.