  Virtual sound synthesis is a technology that allows users to perceive spatial
sound through headphones or earphones. However, accurate virtual sound requires
an individual head-related transfer function (HRTF), which can be difficult to
measure due to the need for a specialized environment. In this study, we
proposed a method to generate HRTFs from one direction to the other. To this
end, we used temporal convolutional neural networks (TCNs) to generate
head-related impulse responses (HRIRs). To train the TCNs, publicly available
datasets in the horizontal plane were used. Using the trained networks, we
successfully generated HRIRs for directions other than the front direction in
the dataset. We found that the proposed method successfully generated HRIRs for
publicly available datasets. To test the generalization of the method, we
measured the HRIRs of a new dataset and tested whether the trained networks
could be used for this new dataset. Although the similarity evaluated by
spectral distortion was slightly degraded, behavioral experiments with human
participants showed that the generated HRIRs were equivalent to the measured
ones. These results suggest that the proposed TCNs can be used to generate
personalized HRIRs from one direction to another, which could contribute to the
personalization of virtual sound.
