Transfer Learning for Underrepresented Music Generation
Anahita Doosti Matthew Guzdial
This paper investigates a combinational creativity approach to transfer
learning to improve the performance of deep neural network-based models for
music generation on out-of-distribution (OOD) genres. We identify Iranian folk
music as an example of such an OOD genre for MusicVAE, a large generative music
model. We find that a combinational creativity transfer learning approach can
efficiently adapt MusicVAE to an Iranian folk music dataset, indicating
potential for generating underrepresented music genres in the future.