Head-Related Transfer Function Interpolation From Spatially Sparse Measurements Using Autoencoder With Source Position Conditioning
Head-Related Transfer Function Interpolation From Spatially Sparse Measurements Using Autoencoder With Source Position Conditioning
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DOI：
10.1109/iwaenc53105.2022.9914751
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Yuki Ito;Tomohiko Nakamura;Shoichi Koyama;H. Saruwatari
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文献类型：
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作者：
Yuki Ito;Tomohiko Nakamura;Shoichi Koyama;H. Saruwatari
我们提出了一种方法的头相关的传递函数（HRTF）插值稀疏测量HRTF使用的自编码器与源位置调节。所提出的方法是从一个HRTF插值方法的基础上正则化线性回归（RLR）和自动编码器之间的类比。通过这种类比，我们发现了基于RLS的方法的关键特征，HRTF被分解为源位置相关和源位置无关的因素。在此基础上，我们设计了编码器和解码器，使得它们的权重和偏置由源位置生成。此外，我们引入了一个聚合模块，减少了源位置上的潜在变量的依赖性，以获得一个源位置独立的表示每个主题。数值实验表明，该方法可以很好地工作，为看不见的主题，并实现了插值性能只有八分之一的测量相媲美的基于RLS的方法。
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 de-sign 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.