  The voice mode of the Opus audio coder can compress wideband speech at bit
rates ranging from 6 kb/s to 40 kb/s. However, Opus is at its core a waveform
matching coder, and as the rate drops below 10 kb/s, quality degrades quickly.
As the rate reduces even further, parametric coders tend to perform better than
waveform coders. In this paper we propose a backward-compatible way of
improving low bit rate Opus quality by re-synthesizing speech from the decoded
parameters. We compare two different neural generative models, WaveNet and
LPCNet. WaveNet is a powerful, high-complexity, and high-latency architecture
that is not feasible for a practical system, yet provides a best known
achievable quality with generative models. LPCNet is a low-complexity,
low-latency RNN-based generative model, and practically implementable on mobile
phones. We apply these systems with parameters from Opus coded at 6 kb/s as
conditioning features for the generative models. A listening test shows that
for the same 6 kb/s Opus bit stream, synthesized speech using LPCNet clearly
outperforms the output of the standard Opus decoder. This opens up ways to
improve the decoding quality of existing speech and audio waveform coders
without breaking compatibility.

Audio and Speech Processing;Sound