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The compression ratio of core-encoder can be improved significantly by reducing the bandwidth of the audio signal, resulting in the poor listening perception. This paper proposes a bandwidth extension method based on generative adversarial nets (GAN) for extending the bandwidth of an audio signal, to create a more natural sound. The method uses GAN as a generative model to fit the distribution of the MDCT coefficients of the audio signals in the high-frequency components. Through minimax two-player gaming, more natural high-frequency information can be estimated. On this basis, a codec system is built up. To evaluate the proposed bandwidth extension system the MUSHRA experiments were carried on and the results show that there is comparable performance with HE-AAC.
Author (s): Huang, Qingbo;
Wu, Xihong;
Qu, Tianshu;
Affiliation:
Peking University, Beijing, China
(See document for exact affiliation information.)
AES Convention: 144
Paper Number:9954
Publication Date:
2018-05-06
Session subject:
Audio Coding, Analysis, and Synthesis
DOI:
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Huang, Qingbo; Wu, Xihong; Qu, Tianshu; 2018; Bandwidth Extension Method Based on Generative Adversarial Nets for Audio Compression [PDF]; Peking University, Beijing, China; Paper 9954; Available from: https://aes.org/publications/elibrary-page/?id=19471
Huang, Qingbo; Wu, Xihong; Qu, Tianshu; Bandwidth Extension Method Based on Generative Adversarial Nets for Audio Compression [PDF]; Peking University, Beijing, China; Paper 9954; 2018 Available: https://aes.org/publications/elibrary-page/?id=19471
@inproceedings{Huang2018bandwidth,
title={{Bandwidth Extension Method Based on Generative Adversarial Nets for Audio Compression}},
author={Huang, Qingbo and Wu, Xihong and Qu, Tianshu},
year={2018},
month={may},
booktitle={Journal of the Audio Engineering Society},
publisher={Paper 9954; AES Convention 144; May 2018},
number={9954},
organization={AES},
}
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