关键词: 生成对抗网络（GAN）, 音质提升, 模型压缩

Abstract: For the convenience of network transmission as well as the ease of server harddisks’ burden, large amount of audio files are compressed, while the audio quality is decreased. This paper proposes an audio quality enhancement algorithm-ASRGAN（Audio Super Resolution Generative Adversarial Networks）-for MPEG-1 Layer 3 files. In this algorithm, the generative network and discrimitive network form a competitive learning, and alternative weighting training is used. Combined with dilation convolution and bidirectional recurrent neural network which evidently enhances the network’s ability in disposing of overlong sequence, the optimal audio quality restoration network is finally set up. This algorithm can reduce the network bandwidth and storage space used by audio files and meanwhile maintain a decent audio quality.

Key words: Generative Adversarial Nets（GAN）, audio enhancement, model compression