  Speech is converted to digital signals using speech coding for efficient
transmission. However, this often lowers the quality and bandwidth of speech.
This paper explores the application of convolutional neural networks for
Artificial Bandwidth Expansion (ABE) and speech enhancement on coded speech,
particularly Adaptive Multi-Rate (AMR) used in 2G cellular phone calls. In this
paper, we introduce AMRConvNet: a convolutional neural network that performs
ABE and speech enhancement on speech encoded with AMR. The model operates
directly on the time-domain for both input and output speech but optimizes
using combined time-domain reconstruction loss and frequency-domain perceptual
loss. AMRConvNet resulted in an average improvement of 0.425 Mean Opinion Score
- Listening Quality Objective (MOS-LQO) points for AMR bitrate of 4.75k, and
0.073 MOS-LQO points for AMR bitrate of 12.2k. AMRConvNet also showed
robustness in AMR bitrate inputs. Finally, an ablation test showed that our
combined time-domain and frequency-domain loss leads to slightly higher MOS-LQO
and faster training convergence than using either loss alone.
