Transfer Learning for Underrepresented Music Generation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 1 Jun 2023.
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7 of 23 references.
References · 7
- 2014FitNets: Hints for Thin Deep Netscited by 1,619
- 2018A Survey on Deep Transfer Learningcited by 417
- 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
- 2017MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generationcited by 72
- 2020A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directionscited by 56
- 2017Deep Learning Techniques for Music Generation -- A Surveycited by 49
- 2021Conceptual Expansion Neural Architecture Search (CENAS)cited by 1
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