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Neural fields have successfully been used in many research fields for theirnative ability to estimate a continuous function from a finite number ofobservations. In audio processing, this technique has been applied to acousticand head-related transfer function interpolation. However, most of the existingmethods estimate the real-valued magnitude function over a predefined discreteset of frequencies. In this study, we propose a novel approach for steeringvector interpolation that regards frequencies as continuous input variables.Moreover, we propose a novel unsupervised regularization term enforcing theestimated filters to be causal. The experiment using real steering vectors showthat the proposed frequency resolution-free method outperformed the existingmethods operating over discrete set of frequencies.

Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions