Autoencoding HRTFS for DNN Based HRTF Personalization Using Anthropometric Features

Abstract

We proposed a deep neural network (DNN) based approach to synthesize the magnitude of personalized head-related transfer functions (HRTFs) using anthropometric features of the user. To mitigate the over-fitting problem when training dataset is not very large, we built an autoencoder for dimensional reduction and establishing a crucial feature set to represent the raw HRTFs. Then we combined the decoder part of the autoencoder with a smaller DNN to synthesize the magnitude HRTFs. In this way, the complexity of the neural networks was greatly reduced to prevent unstable results with large variance due to overfitting. The proposed approach was compared with a baseline DNN model with no autoencoder. The log-spectral distortion (LSD) metric was used to evaluate the performance. Experiment results show that the proposed approach can reduce LSD of estimated HRTFs with greater stability.