  Audio coding is an essential module in the real-time communication system.
Neural audio codecs can compress audio samples with a low bitrate due to the
strong modeling and generative capabilities of deep neural networks. To address
the poor high-frequency expression and high computational cost and storage
consumption, we proposed an integrated framework that utilizes a neural network
to model wide-band components and adopts traditional signal processing to
compress high-band components according to psychological hearing knowledge.
Inspired by auditory perception theory, a perception-based loss function is
designed to improve harmonic modeling. Besides, generative adversarial network
(GAN) compression is proposed for the first time for neural audio codecs. Our
method is superior to prior advanced neural codecs across subjective and
objective metrics and allows real-time inference on desktop and mobile.
