  This work presents a scalable and efficient neural waveform codec (NWC) for
speech compression. We formulate the speech coding problem as an autoencoding
task, where a convolutional neural network (CNN) performs encoding and decoding
as its feedforward routine. The proposed CNN autoencoder also defines
quantization and entropy coding as a trainable module, so the coding artifacts
and bitrate control are handled during the optimization process. We achieve
efficiency by introducing compact model architectures to our fully
convolutional network model, such as gated residual networks and depthwise
separable convolution. Furthermore, the proposed models are with a scalable
architecture, cross-module residual learning (CMRL), to cover a wide range of
bitrates. To this end, we employ the residual coding concept to concatenate
multiple NWC autoencoding modules, where an NWC module performs residual coding
to restore any reconstruction loss that its preceding modules have created.
CMRL can scale down to cover lower bitrates as well, for which it employs
linear predictive coding (LPC) module as its first autoencoder. We redefine
LPC's quantization as a trainable module to enhance the bit allocation tradeoff
between LPC and its following NWC modules. Compared to the other autoregressive
decoder-based neural speech coders, our decoder has significantly smaller
architecture, e.g., with only 0.12 million parameters, more than 100 times
smaller than a WaveNet decoder. Compared to the LPCNet-based speech codec,
which leverages the speech production model to reduce the network complexity in
low bitrates, ours can scale up to higher bitrates to achieve transparent
performance. Our lightweight neural speech coding model achieves comparable
subjective scores against AMR-WB at the low bitrate range and provides
transparent coding quality at 32 kbps.
