The Style Transformation of Gu-Zheng Music Based on the Improved Model of Cyclegan

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Yifan Li, Xinyu Hu


In recent years, generative adversarial networks have excelled in the field of image style migration, however their performance in the field of music has been mediocre. Existing music style migration does not work well for style migration of gu-zheng music. In order to solve these problems, we first extract the features of gu-zheng music and the Mel-spectrum features, then use CycleGAN to do style transformation on the combined features and Mel-spectrum features, and then use WaveNet vocoder to decode the migrated spectrograms, and finally achieve the style migration with gu-zheng music. The proposed model was evaluated on the publicly available dataset FMA, and the average style migration rate of the compliant music reached 94.07%. Compared to other algorithms, the music produced by this method outperformed other algorithms in terms of style migration rate and audio quality.

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