Abstract:

This paper presents SuDaField, a subject- and dataset-aware neural field (NF) that can leverage multiple head-related transfer function (HRTF) datasets. NF-based HRTF modeling has gained much attention because its grid-agnostic formulation accommodates any spatial grids during training and inference. While NFs are grid-agnostic, their training on multiple datasets remains challenging, as HRTFs from different datasets exhibit distinct characteristics due to variations in measurement setups. To mitigate this issue, Task 1 of the Listener Acoustic Personalization (LAP) Challenge 2024 proposed the task of HRTF harmonization, which aims to compensate for dataset-specific effects while preserving spatial cues of the original HRTFs. The harmonization itself is still hindered by the difference in spatial grids and the ill-defined nature of ideal harmonized HRTFs. We thus propose a well-defined framework of HRTF conversion and realize this by concurrently performing NF training and disentanglement of subject- and dataset-specific information. Our NF adopts dataset-specific parameters shared across all subjects within each dataset, with these parameters capturing the influence of the measurement setups. By replacing the dataset-specific parameters with those of another dataset, we can convert HRTFs recorded in one environment to what they would be if recorded in another environment. Our experimental results show that the dataset-specific parameters allow us to effectively perform HRTF conversion, achieving state-of- the-art performance on Task 1 of the LAP Challenge 2024.