  Conventional audio coding technologies commonly leverage human perception of
sound, or psychoacoustics, to reduce the bitrate while preserving the
perceptual quality of the decoded audio signals. For neural audio codecs,
however, the objective nature of the loss function usually leads to suboptimal
sound quality as well as high run-time complexity due to the large model size.
In this work, we present a psychoacoustic calibration scheme to re-define the
loss functions of neural audio coding systems so that it can decode signals
more perceptually similar to the reference, yet with a much lower model
complexity. The proposed loss function incorporates the global masking
threshold, allowing the reconstruction error that corresponds to inaudible
artifacts. Experimental results show that the proposed model outperforms the
baseline neural codec twice as large and consuming 23.4% more bits per second.
With the proposed method, a lightweight neural codec, with only 0.9 million
parameters, performs near-transparent audio coding comparable with the
commercial MPEG-1 Audio Layer III codec at 112 kbps.
