  Fully convolutional recurrent neural networks (FCRNs) have shown
state-of-the-art performance in single-channel speech enhancement. However, the
number of parameters and the FLOPs/second of the original FCRN are
restrictively high. A further important class of efficient networks is the
CRUSE topology, serving as reference in our work. By applying a number of
topological changes at once, we propose both an efficient FCRN (FCRN15), and a
new family of efficient convolutional recurrent neural networks (EffCRN23,
EffCRN23lite). We show that our FCRN15 (875K parameters) and EffCRN23lite
(396K) outperform the already efficient CRUSE5 (85M) and CRUSE4 (7.2M)
networks, respectively, w.r.t. PESQ, DNSMOS and DeltaSNR, while requiring about
94% less parameters and about 20% less #FLOPs/frame. Thereby, according to
these metrics, the FCRN/EffCRN class of networks provides new best-in-class
network topologies for speech enhancement.
