Transfer Learning for Underrepresented Music Generation

• URL: http://arxiv.org/abs/2306.00281v1
• Date: Thu, 1 Jun 2023 01:53:10 GMT
• Title: Transfer Learning for Underrepresented Music Generation
• Authors: Anahita Doosti and Matthew Guzdial
• Abstract summary: 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.
• Score: 0.9645196221785693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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