Learning-based Array Configuration-Independent Binaural Audio Telepresence with Scalable Signal Enhancement and Ambience Preservation

Abstract: Audio Telepresence (AT) aims to create an immersive experience of the audio scene at the far end for the user(s) at the near end. The application of AT could encompass scenarios with varying degrees of emphasis on signal enhancement and ambience preservation. It is desirable for an AT system to be scalable between these two extremes. To this end, we propose an array-based Binaural AT (BAT) system using the DeepFilterNet as the backbone to convert the array microphone signals into the Head-Related Transfer Function (HRTF)-filtered signals, with a tunable weighting between signal enhancement and ambience preservation. An array configuration-independent Spatial COherence REpresentation (SCORE) feature is proposed for the model training so that the network remains robust to different array geometries and sensor counts. magnitude-weighted Interaural Phase Difference error (mw-IPDe), magnitude-weighted Interaural Level Difference error (mw-ILDe), and modified Scale-Invariant Signal-to-Distortion Ratio (mSI-SDR) are defined as performance metrics for objective evaluation. Subjective listening tests were also performed to validate the proposed BAT system. The results have shown that the proposed BAT system can achieve superior telepresence performance with the desired balance between signal enhancement and ambience preservation, even when the array configurations are unseen in the training phase.