HRTF Upsampling Across Varying Measurement Configurations with Geometry-Aware Query-Conditioned Aggregation

Personalized head-related transfer functions (HRTFs) are essential for spatial audio rendering, but densely measuring an individual's HRTFs is costly and time-consuming. HRTF upsampling reduces this burden by estimating dense HRTFs from sparse measurements. Recent learning-based methods have achieved promising performance, but many remain tied to predefined measurement configurations. In this work, we propose GeoAtt, a variable-context HRTF upsampling framework that uses a single trained model across varying measurement configurations. GeoAtt performs geometry-aware, query-conditioned spatial aggregation over the available measurements independently at each frequency bin, followed by frequency-domain modeling using Conformer blocks. The relative geometry between the target and measured directions is incorporated as an additive bias in the cross-attention. Experiments on the SONICOM dataset show that a single trained model achieves the lowest log-spectral distortion across all four canonical Listener Acoustic Personalization (LAP) challenge measurement configurations and further generalizes to configurations that are not explicitly included during training.