DACD-HRTF: A Direction-Aware Contrastive Disentanglement Framework for Cross-Dataset Personalized HRTF Upsampling

Personalized head-related transfer function (HRTF) modeling is essential for immersive spatial audio rendering. However, acquiring high-resolution HRTFs is laborious, as it requires measurements from hundreds or thousands of spatial directions. To alleviate this burden, recent approaches either estimate HRTFs from anatomical data or reconstruct high-resolution HRTFs from a small set of measured directions. Existing methods, however, are often limited to single-dataset training and may fail to generalize to new environments due to dataset-specific measurement biases (e.g., device frequency responses and recording environment setup). In this work, we propose DACD-HRTF, a cross-dataset personalized HRTF upsampling framework based on sparse HRTF measurements. Specifically, DACD-HRTF integrates a dual-branch direction-aware autoencoder to encode sparse measurements into subject-related and dataset-related representations, which are then decoded to estimate personalized HRTFs over high-resolution spatial directions. This design supports joint training across multiple datasets with heterogeneous spatial sampling grids and measurement conditions. To further mitigate measurement biases across datasets, we introduce a contrastive-based disentanglement strategy that encourages the separation of subject-related and dataset-related information, thereby enhancing cross-dataset generalization. Crucially, our framework enables direct reconstruction from sparse measurements without fine-tuning of network parameters at inference, allowing rapid deployment in new environments. Extensive experiments on eight public HRTF datasets demonstrate that DACD-HRTF achieves competitive performance in reconstructing personalized HRTFs for unseen datasets, showing particular effectiveness under practical sparse measurement settings.