Auditory-model-based assessment of the effect of head-worn devices on sound localisation

Head-worn devices (HWDs), e.g. headphones, head-mounted displays or helmets, inherently introduce acoustic distortions to the sound reaching the ear canals, potentially degrading the localisation abilities of the listener. These distortions pose potential risks to safety, hinder spatial awareness, and may affect immersion in augmented reality applications. Traditional methods for assessing the degradation in localisation due to HWDs rely on listening experiments, which are time-consuming and require specific facilities. Consequently, alternative approaches are sought, particularly in the prototyping and development phases of HWDs. This thesis investigates the feasibility of utilising acoustic measurements and auditory models to estimate the degradation in localisation caused by HWDs. We examine the efficacy of existing static localisation models in predicting experimental data when HWDs are worn. These models demonstrate robustness in their predictions, despite their initial validation under open-ear conditions only. Furthermore, we propose two auditory models tailored to estimating degradation in localisation with HWDs. The first model combines a peripheral processing front-end with a shallow neural network that estimates perceived localisation from frequency-dependent interaural cues. The second model extends an existing static localisation model based on Bayesian inference to accommodate voluntary head rotations in an auditory-aided visual search task. We validate these models with experimental data and provide publicly available implementations. Our contributions aim to enhance automatic assessment tools for HWD quality by leveraging advancements in sound localisation research. The performance of these models is robust for unseen listening conditions, highlighting the importance of integrating evidence from hearing research into assessment methodologies. This motivates the need for ongoing fundamental research in sound localisation and the development of auditory models that incorporate such findings, with the overarching goal of enhancing the quality of spatial audio applications.