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arXiv · 2501.02871

Towards HRTF Personalization using Denoising Diffusion Models

Juan Camilo Albarracín Sánchez·Luca Comanducci·Mirco Pezzoli·Fabio Antonacci
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Abstract

Head-Related Transfer Functions (HRTFs) have fundamental applications for realistic rendering in immersive audio scenarios. However, they are strongly subject-dependent as they vary considerably depending on the shape of the ears, head and torso. Thus, personalization procedures are required for accurate binaural rendering. Recently, Denoising Diffusion Probabilistic Models (DDPMs), a class of generative learning techniques, have been applied to solve a variety of signal processing-related problems. In this paper, we propose a first approach for using DDPM conditioned on anthropometric measurements to generate personalized Head-Related Impulse Response (HRIR), the time-domain representation of HRTF. The results show the feasibility of DDPMs for HRTF personalization obtaining performance in line with state-of-the-art models.

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Juan Camilo Albarracín Sánchez, Luca Comanducci, Mirco Pezzoli, Fabio Antonacci. 2025-01-06. Towards HRTF Personalization using Denoising Diffusion Models. https://doi.org/10.1109/icassp49660.2025.10887566

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