  Neural speech coding is a rapidly developing topic, where state-of-the-art
approaches now exhibit superior compression performance than conventional
methods. Despite significant progress, existing methods still have limitations
in preserving and reconstructing fine details for optimal reconstruction,
especially at low bitrates. In this study, we introduce SuperCodec, a neural
speech codec that achieves state-of-the-art performance at low bitrates. It
employs a novel back projection method with selective feature fusion for
augmented representation. Specifically, we propose to use Selective Up-sampling
Back Projection (SUBP) and Selective Down-sampling Back Projection (SDBP)
modules to replace the standard up- and down-sampling layers at the encoder and
decoder, respectively. Experimental results show that our method outperforms
the existing neural speech codecs operating at various bitrates. Specifically,
our proposed method can achieve higher quality reconstructed speech at 1 kbps
than Lyra V2 at 3.2 kbps and Encodec at 6 kbps.
