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.
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.
