论文标题
使用具有源位置调节的自动编码器从空间稀疏测量中进行与头部相关的传递函数插值
Head-Related Transfer Function Interpolation from Spatially Sparse Measurements Using Autoencoder with Source Position Conditioning
论文作者
论文摘要
我们建议使用具有源位置调节的自动编码器从稀疏测量的HRTF中插值的头部相关传输函数（HRTF）的方法。提出的方法是根据基于正规化线性回归（RLR）和自动编码器的HRTF插值方法的类比来得出的。通过这种类比，我们发现了基于RLR的方法的关键特征，即HRTF被分解为源位置依赖性和源位独立因素。根据这一发现，我们设计了编码器和解码器，以便它们的权重和偏见是从源位置产生的。此外，我们引入了一个聚合模块，该模块降低了潜在变量对源位置的依赖性，以获取每个受试者的源位无关表示。数值实验表明，所提出的方法可以很好地适用于看不见的受试者，并实现仅与基于RLR的方法相当的八分之一的测量值。
We propose a method of head-related transfer function (HRTF) interpolation from sparsely measured HRTFs using an autoencoder with source position conditioning. The proposed method is drawn from an analogy between an HRTF interpolation method based on regularized linear regression (RLR) and an autoencoder. Through this analogy, we found the key feature of the RLR-based method that HRTFs are decomposed into source-position-dependent and source-position-independent factors. On the basis of this finding, we design the encoder and decoder so that their weights and biases are generated from source positions. Furthermore, we introduce an aggregation module that reduces the dependence of latent variables on source position for obtaining a source-position-independent representation of each subject. Numerical experiments show that the proposed method can work well for unseen subjects and achieve an interpolation performance with only one-eighth measurements comparable to that of the RLR-based method.
