Progressive Multi-Stage Neural Audio Coding with Guided References

International Conference
2022-01-24 18:47
Authors : Chanwoo Lee, Hyungseob Lim, Jihyun Lee, Inseon Jang, Hong-Goo Kang

Year : 2022

Publisher / Conference : ICASSP

Research area : Audio Signal Processing, Coding

Related project : 생성모델 기반 음향압축 기술 연구(2/5)

Presentation : Poster

In this paper, we propose an effective multi-stage neural audio coding algorithm that encodes full-band audio signals (up to 20 kHz) using an end-to-end training criterion. By pre defining several dyadic subband signals as training targets, we progressively encode input audio signals in each stage such that deeper stages of the network encode the residual error terms from the previous encoding stage. Our proposed audio codec successfully decodes full-band audio signals by using an effective multi-stage vector quantization scheme to represent key encoding features extracted in the latent space. Subjective listening tests show that the decoded outputs of the proposed audio codec are indistinguishable from the uncoded inputs at an average bitrate of 136 kbps.
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