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Telepresence aims to create an immersive but virtual experience of the audioand visual scene at the far end for users at the near end. In thiscontribution, we propose an array-based binaural rendering system that convertsthe array microphone signals into the head-related transfer function (HRTF)filtered output signals for headphone-rendering. The proposed approach isformulated in light of a model-matching principle (MMP) and is capable ofdelivering more immersive experience than the conventionallocalization-beamforming-HRTF filtering (LBH) approach. The MMP-based renderingsystem can be realized via multichannel inverse filtering (MIF) andmultichannel deep filtering (MDF). In this study, we adopted the MDF approachand used the LBH as well as MIF as the baselines. The all-neural system jointlycaptures the spatial information (spatial rendering), preserves ambient sound(enhancement), and reduces noise (enhancement) before generating binauraloutputs. Objective and subjective tests are employed to compare the proposedtelepresence system with two baselines.

Model-matching Principle Applied to the Design of an Array-based All-neural Binaural Rendering System for Audio Telepresence