BSM-iMagLS: ILD Informed Binaural Signal Matching for Reproduction with Head-Mounted Microphone Arrays

TL;DR

This paper introduces BSM-iMagLS, an advanced binaural signal matching method that incorporates ILD into MagLS optimization, significantly improving spatial fidelity in headphone reproduction for AR and VR applications.

Contribution

It extends BSM with ILD integration and a DNN-based solver, enhancing binaural reproduction quality with joint optimization of magnitude and ILD.

Findings

Reduces ILD errors substantially

Maintains magnitude accuracy comparable to state-of-the-art

Validated through simulations and listening tests

Abstract

Headphone listening in applications such as augmented and virtual reality (AR and VR) relies on high-quality spatial audio to ensure immersion, making accurate binaural reproduction a critical component. As capture devices, wearable arrays with only a few microphones with irregular arrangement face challenges in achieving a reproduction quality comparable to that of arrays with a large number of microphones. Binaural signal matching (BSM) has recently been presented as a signal-independent approach for generating high-quality binaural signal using only a few microphones, which is further improved using magnitude-least squares (MagLS) optimization at high frequencies. This paper extends BSM with MagLS by introducing interaural level difference (ILD) into the MagLS, integrated into BSM (BSM-iMagLS). Using a deep neural network (DNN)-based solver, BSM-iMagLS achieves joint optimization of magnitude, ILD, and magnitude derivatives, improving spatial fidelity. Performance is validated through theoretical analysis, numerical simulations with diverse HRTFs and head-mounted array geometries, and listening experiments, demonstrating a substantial reduction in ILD errors while maintaining comparable magnitude accuracy to state-of-the-art solutions. The results highlight the potential of BSM-iMagLS to enhance binaural reproduction for wearable and portable devices.