Learning to Personalize Equalization for High-Fidelity Spatial Audio Reproduction

Reproducing accurate and perceptually realistic spatial audio for augmented and virtual reality (AR/VR) requires the headphones to have a flat frequency response. This can be achieved by equalizing the headphone transducers' output given the transfer function between the transducer and the human ear, referred to as Ear Acoustic Response (EAR). EAR is unique to every individual and is a function of the transducer characteristics, the user's anthropometric features (e.g. ear and head shape) and the interactions between the two. This paper proposes a novel method to infer the EAR given the ear features of any listener using a probabilistic framework and a sub-sample of the population as prior. We introduce an approach to assess the level of personalization achieved and benchmark the improvements delivered by the proposed algorithm relative to a generic solution.