  Speech codec enhancement methods are designed to remove distortions added by
speech codecs. While classical methods are very low in complexity and add zero
delay, their effectiveness is rather limited. Compared to that, DNN-based
methods deliver higher quality but they are typically high in complexity and/or
require delay. The recently proposed Linear Adaptive Coding Enhancer (LACE)
addresses this problem by combining DNNs with classical long-term/short-term
postfiltering resulting in a causal low-complexity model. A short-coming of the
LACE model is, however, that quality quickly saturates when the model size is
scaled up. To mitigate this problem, we propose a novel adatpive temporal
shaping module that adds high temporal resolution to the LACE model resulting
in the Non-Linear Adaptive Coding Enhancer (NoLACE). We adapt NoLACE to enhance
the Opus codec and show that NoLACE significantly outperforms both the Opus
baseline and an enlarged LACE model at 6, 9 and 12 kb/s. We also show that LACE
and NoLACE are well-behaved when used with an ASR system.
