Ambisonics Binaural Rendering via Masked Magnitude Least Squares

TL;DR

This paper introduces Masked Magnitude Least Squares, a neural network-based method for Ambisonics binaural rendering that improves high-frequency localization cues while maintaining low-order computational efficiency.

Contribution

It proposes a novel neural network approach with a spatio-spectral weighting mask to enhance Ambisonics HRTF approximation, especially at high frequencies.

Findings

Improved median plane localization performance.

Maintained high-frequency notches in low-order HRTFs.

Marginal impact on overall magnitude reconstruction accuracy.

Abstract

Ambisonics rendering has become an integral part of 3D audio for headphones. It works well with existing recording hardware, the processing cost is mostly independent of the number of sound sources, and it elegantly allows for rotating the scene and listener. One challenge in Ambisonics headphone rendering is to find a perceptually well behaved low-order representation of the Head-Related Transfer Functions (HRTFs) that are contained in the rendering pipe-line. Low-order rendering is of interest, when working with microphone arrays containing only a few sensors, or for reducing the bandwidth for signal transmission. Magnitude Least Squares rendering became the de facto standard for this, which discards high-frequency interaural phase information in favor of reducing magnitude errors. Building upon this idea, we suggest Masked Magnitude Least Squares, which optimized the Ambisonics coefficients with a neural network and employs a spatio-spectral weighting mask to control the accuracy of the magnitude reconstruction. In the tested case, the weighting mask helped to maintain high-frequency notches in the low-order HRTFs and improved the modeled median plane localization performance in comparison to MagLS, while only marginally affecting the overall accuracy of the magnitude reconstruction.