Upmix B-Format Ambisonic Room Impulse Responses Using a Generative Model

Abstract

Ambisonic room impulse responses (ARIRs) are recorded to capture the spatial acoustic characteristics of specific rooms, with widespread applications in virtual and augmented reality. While the first-order Ambisonics (FOA) microphone array is commonly employed for three-dimensional (3D) room acoustics recording due to its easy accessibility, higher spatial resolution necessitates using higher-order Ambisonics (HOA) in applications such as binaural rendering and sound field reconstruction. This paper introduces a novel approach, leveraging generative models to upmix ARIRs. The evaluation results validate the model’s effectiveness at upmixing first-order ARIRs to higher-order representations, surpassing the aliasing frequency limitations. Furthermore, the spectral errors observed in the Binaural Room Transfer Functions (BRTFs) indicate the potential benefits of using upmixed ARIRs for binaural rendering, significantly improving rendering accuracy.

Keywords

• Ambisonics
• upmix
• generative model