This paper shows the feasibility of a variant of the Generative Adversarial Network (GAN), called Star GAN, for music genre transfer. This method is noteworthy in that it simultaneously learns many-to-many mappings across different attribute domains using a single generator network. A similar architecture to research in MuseGAN and CycleGAN is applied. Also, as in MGTGAN, Desert Camel MIDI dataset is use for training and testing.
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