Modeling of Individual Head-Related Transfer Functions (HRTFs) Based on Spatiotemporal and Anthropometric Features Using Deep Neural Networks

This paper presents an innovative approach for personalized modeling of Head-Related Transfer Functions (HRTFs) using deep neural networks (DNNs), with a primary focus on addressing interaural time differences (ITDs), torso and pinna anthropometrics. HRTFs play a pivotal role in spatial hearing applications, such as virtual auditory displays and hearing assistive devices. The core objectives of this study involve mitigating measurement irregularities related to sudden head movements, microphone and sound source positioning, and head orientation. A significant component of this paper introduces a novel ITD correction method designed to reduce irregularities within the HRTF database. This method leverages ITD data, in conjunction with torso and pinna anthropometrics, within a DNN-based model to estimate individual HRTFs for precise spatial directions. Comparisons are made between this approach and traditional head measurement methods. Additionally, this research investigates the sensitivity of HRTFs to minor changes in head position. By analyzing variations in left and right pinna HRTFs at different head tilt angles through numerical simulations, using a 3D model of a human head and torso, the study quantifies spectral distortion in HRTFs. It highlights that spectral distortion generally increases with head motion and sound signal frequency, with a minor decrease observed at 16 kHz. In summary, this paper contributes to the advancement of personalized HRTF modeling, particularly in scenarios involving fluctuations in head position. The findings have the potential to enhance the effectiveness of machine learning algorithms for sound localization, particularly when considering both pinnae HRTFs. This research underscores the importance of these aspects in optimizing spatial hearing experiences.