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학술대회 Progressive Multi-Stage Neural Audio Coding with Guided References
Cited 4 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
저자
이찬우, 임형섭, 이지현, 장인선, 강홍구
발행일
202205
출처
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022, pp.876-880
DOI
https://dx.doi.org/10.1109/ICASSP43922.2022.9747527
협약과제
21ZH1200, 초실감 입체공간 미디어·콘텐츠 원천기술연구, 이태진
초록
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 predefining 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 achieve almost transparent quality at an average bitrate of 132 kbps.
KSP 제안 키워드
Audio codec, Audio coding, Audio signal, End to End(E2E), Full-band, Latent space, Multi-stage vector quantization, Residual error, coding algorithm, end-to-end training, subjective listening tests