Spatial Upsampling of Head-Related Transfer Function Using Neural Network Conditioned on Source Position and Frequency

Abstract

A spatial upsampling method for the head-related transfer function (HRTF) using deep neural networks (DNNs), consisting of an autoencoder conditioned on the source position and frequency, is proposed. On the basis of our finding that the conventional regularized linear regression (RLR)-based upsampling method can be reinterpreted as a linear autoencoder, we designed our network architecture as a nonlinear extension of the RLR-based method, whose key features are the encoder and decoder weights depending on the source positions and the latent variables independent of the source positions. We also extend this architecture to upsample HRTFs and interaural time differences (ITDs) in a single network, which allows us to efficiently obtain head-related impulse responses (HRIRs). Experimental results on upsampling accuracy and perceptual quality indicated that our proposed method can upsample HRTFs from sparse measurements with sufficient quality.