  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.

Audio and Speech Processing