  Communication technologies like voice over IP operate under constrained
real-time conditions, with voice packets being subject to delays and losses
from the network. In such cases, the packet loss concealment (PLC) algorithm
reconstructs missing frames until a new real packet is received. Recently,
autoregressive deep neural networks have been shown to surpass the quality of
signal processing methods for PLC, specially for long-term predictions beyond
60 ms. In this work, we propose a non-autoregressive adversarial auto-encoder,
named PLAAE, to perform real-time PLC in the waveform domain. PLAAE has a
causal convolutional structure, and it learns in an auto-encoder fashion to
reconstruct signals with gaps, with the help of an adversarial loss. During
inference, it is able to predict smooth and coherent continuations of such gaps
in a single feed-forward step, as opposed to autoregressive models. Our
evaluation highlights the superiority of PLAAE over two classic PLCs and two
deep autoregressive models in terms of spectral and intonation reconstruction,
perceptual quality, and intelligibility.
