Ambisonics Super-Resolution Using A Waveform-Domain Neural Network

Ismael Nawfal; Symeon Delikaris Manias; Mehrez Souden; Juha Merimaa; Joshua Atkins; Elisabeth McMullin; Shadi Pirhosseinloo; Daniel Phillips

arXiv · 2508.00240

Ambisonics Super-Resolution Using A Waveform-Domain Neural Network

Ismael Nawfal·Symeon Delikaris Manias·Mehrez Souden·Juha Merimaa·Joshua Atkins·Elisabeth McMullin·Shadi Pirhosseinloo·Daniel Phillips

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Abstract

Ambisonics is a spatial audio format describing a sound field. First-order Ambisonics (FOA) is a popular format comprising only four channels. This limited channel count comes at the expense of spatial accuracy. Ideally one would be able to take the efficiency of a FOA format without its limitations. We have devised a data-driven spatial audio solution that retains the efficiency of the FOA format but achieves quality that surpasses conventional renderers. Utilizing a fully convolutional time-domain audio neural network (Conv-TasNet), we created a solution that takes a FOA input and provides a higher order Ambisonics (HOA) output. This data driven approach is novel when compared to typical physics and psychoacoustic based renderers. Quantitative evaluations showed a 0.6dB average positional mean squared error difference between predicted and actual 3rd order HOA. The median qualitative rating showed an 80% improvement in perceived quality over the traditional rendering approach.

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BibTeX RIS

Ismael Nawfal, Symeon Delikaris Manias, Mehrez Souden, Juha Merimaa, Joshua Atkins, Elisabeth McMullin, Shadi Pirhosseinloo, Daniel Phillips. 2025-08-01. Ambisonics Super-Resolution Using A Waveform-Domain Neural Network. https://arxiv.org/abs/2508.00240

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