Open AccessMusic Style Transfer with Vocals Based on CycleGAN
Author(s) -
Hongliang Ye,
Wanning Zhu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1631/1/012039
Subject(s) - generative grammar , spectrogram , adversarial system , style (visual arts) , classifier (uml) , computer science , generative adversarial network , transfer of learning , artificial neural network , artificial intelligence , speech recognition , deep learning , visual arts , art
In recent years, with the development of generative adversarial networks (GAN), the application of generative adversarial networks has gradually matured. An important application area for generating adversarial networks is called neural style transfer. In recent years, neural style transfer has played a major role in the field of image applications. However, it performed poorly in the music field. In addition, algorithms in the field of music style transfer have poor effect on the style transfer of music with vocals. Therefore, this paper extracts the CQT features and Mel spectrogram features of music, and then uses CycleGAN to transfer the styles of the CQT features and Mel spectrogram mapping pictures, and finally realizes the style transfer of music. On the classifier we trained, the average style transfer rate of music that meets our requirements reached 94.07%.