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Audio Telepresence (AT) aims to create an immersive experience of the audioscene at the far end for the user(s) at the near end. The application of ATcould encompass scenarios with varying degrees of emphasis on signalenhancement and ambience preservation. It is desirable for an AT system to bescalable between these two extremes. To this end, we propose an array-basedBinaural AT (BAT) system using the DeepFilterNet as the backbone to convert thearray microphone signals into the Head-Related Transfer Function(HRTF)-filtered signals, with a tunable weighting between signal enhancementand ambience preservation. An array configuration-independent Spatial COherenceREpresentation (SCORE) feature is proposed for the model training so that thenetwork 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 modifiedScale-Invariant Signal-to-Distortion Ratio (mSI-SDR) are defined as performancemetrics for objective evaluation. Subjective listening tests were alsoperformed to validate the proposed BAT system. The results have shown that theproposed BAT system can achieve superior telepresence performance with thedesired balance between signal enhancement and ambience preservation, even whenthe array configurations are unseen in the training phase.

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