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Sound field reconstruction using spherical harmonics (SH) has been widely used. However, order-limited summation leads to an inaccurate reconstruction of sound pressure when the reconstructed region is large. The reconstruction performance also degrades when it comes to high frequency. Upscaling ambisonic sound scenes is used to overcome the limitations. In this work, a deep-learning-based method for upscaling is proposed. Specifically, the generative adversarial network (GAN) is introduced. Instead of estimating the SH coefficients, a U-Net-based fully convolutional generator is introduced, which directly outputs the two-dimensional sound pressure. Results show that the proposed method significantly improves the upscaling results compared with the previous deep-learning-based method.
Author (s): Wang, Yiwen;
Wu, Xihong;
Qu, Tianshu;
Affiliation:
Peking University, Beijing, China
(See document for exact affiliation information.)
AES Convention: 152
Paper Number:10577
Publication Date:
2022-05-06
Session subject:
Spatial Audio
DOI:
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Wang, Yiwen; Wu, Xihong; Qu, Tianshu; 2022; UP-WGAN: Upscaling Ambisonic Sound Scenes Using Wasserstein Generative Adversarial Networks [PDF]; Peking University, Beijing, China; Paper 10577; Available from: https://aes.org/publications/elibrary-page/?id=21690
Wang, Yiwen; Wu, Xihong; Qu, Tianshu; UP-WGAN: Upscaling Ambisonic Sound Scenes Using Wasserstein Generative Adversarial Networks [PDF]; Peking University, Beijing, China; Paper 10577; 2022 Available: https://aes.org/publications/elibrary-page/?id=21690
@inproceedings{Wang2022up-wgan:,
title={{UP-WGAN: Upscaling Ambisonic Sound Scenes Using Wasserstein Generative Adversarial Networks}},
author={Wang, Yiwen and Wu, Xihong and Qu, Tianshu},
year={2022},
month={may},
booktitle={Journal of the Audio Engineering Society},
publisher={Paper 10577; AES Convention 152; May 2022},
number={10577},
organization={AES},
}
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