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A High Fidelity and Low Complexity Neural Audio Coding (2310.10992v1)

Published 17 Oct 2023 in cs.SD and eess.AS

Abstract: 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.

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Authors (9)
  1. Wenzhe Liu (28 papers)
  2. Wei Xiao (100 papers)
  3. Meng Wang (1063 papers)
  4. Shan Yang (58 papers)
  5. Yupeng Shi (11 papers)
  6. Yuyong Kang (1 paper)
  7. Dan Su (101 papers)
  8. Shidong Shang (10 papers)
  9. Dong Yu (329 papers)
Citations (2)

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