  This paper introduces a real-time time-domain packet loss concealment (PLC)
neural-network (tPLCnet). It efficiently predicts lost frames from a short
context buffer in a sequence-to-one (seq2one) fashion. Because of its seq2one
structure, a continuous inference of the model is not required since it can be
triggered when packet loss is actually detected. It is trained on 64h of
open-source speech data and packet-loss traces of real calls provided by the
Audio PLC Challenge. The model with the lowest complexity described in this
paper reaches a robust PLC performance and consistent improvements over the
zero-filling baseline for all metrics. A configuration with higher complexity
is submitted to the PLC Challenge and shows a performance increase of 1.07
compared to the zero-filling baseline in terms of PLC-MOS on the blind test set
and reaches a competitive 3rd place in the challenge ranking.
