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Bandwidth Extension Method Based on Generative Adversarial Nets for Audio Compression

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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.

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