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A Generative Adversarial Net-Based Bandwidth Extension Method for Audio Compression

机译：一种基于对抗网络的生成式音频压缩带宽扩展方法

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摘要

The high frequency components of the audio signal are often truncated during the encoding processing by a lossy codec. To avoid the sound quality degradation, the high frequency components are reconstructed during the decoding processing. This paper presents a new bandwidth extension method for audio compression. Frequency components of 6.9 - 13.8 kHz are added using side information at 2 kbps. A generative neural network in the GAN is used to estimate relationship between the MDCT spectrum in the high frequency part and the low frequency part, and it is evaluated by a discriminant network in the GAN to get a more natural result. On this basis, a codec system is built up. The MUSHRA experiments show that the proposed method is comparable with SBR in HE-AAC.
机译：音频信号的高频分量经常在编码过程中被有损编解码器截断。为了避免声音质量下降，在解码处理期间重构高频分量。本文提出了一种新的音频压缩带宽扩展方法。使用2 kbps的边信息添加6.9-13.8 kHz的频率分量。 GAN中的生成神经网络用于估计高频部分和低频部分中的MDCT频谱之间的关系，并通过GAN中的判别网络对其进行评估，以获得更自然的结果。在此基础上，建立了编解码系统。 MUSHRA实验表明，该方法与HE-AAC中的SBR相当。

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    《Journal of the Audio Engineering Society》 |2019年第12期|986-993|共8页
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    Key Laboratory on Machine Perception (Ministry of Education) Speech and Hearing Research Center Peking University Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Beijing China;

    State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Beijing China;

    Key Laboratory on Machine Perception (Ministry of Education) Speech and Hearing Research Center Peking University Beijing China;

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