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An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation

Publication ,  Conference
Hahn, S; Zhu, R; Mak, S; Rudin, C; Jiang, Y
Published in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 6, 2023

The fast-growing demand for algorithmic music generation is found throughout entertainment, art, education, etc. Unfortunately, most recent models are practically impossible to interpret or musically fine-tune, as they use deep neural networks with thousands of parameters. We introduce an interpretable, flexible, and interactive model, SchenkComposer, for melody generation that empowers users to be creative in all aspects of the music generation pipeline and allows them to learn from the process. We divide the task of melody generation into steps based on the process that a human composer using music-theoretical domain knowledge might use. First, the model determines phrase structure based on form analysis and identifies an appropriate number of measures. Using concepts from Schenkerian analysis, the model then finds a fitting harmonic rhythm, middleground harmonic progression, foreground rhythm, and melody in a hierarchical, scaffolded approach using a probabilistic context-free grammar based on musical contours. By incorporating theories of musical form and harmonic structure, our model produces music with long-term structural coherence. In extensive human experiments, we find that music generated with our approach successfully passes a Turing test in human experiments while current state-of-the-art approaches fail, and we further demonstrate superior performance and preference for our melodies compared to existing melody generation methods. Additionally, we developed and deployed a public website for SchenkComposer, and conducted preliminary user surveys. Through analysis, we show the strong viability and enjoyability of SchenkComposer.

Duke Scholars

Published In

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

DOI

ISBN

9798400701030

Publication Date

August 6, 2023

Start / End Page

4089 / 4099
 

Citation

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Hahn, S., Zhu, R., Mak, S., Rudin, C., & Jiang, Y. (2023). An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4089–4099). https://doi.org/10.1145/3580305.3599772
Hahn, S., R. Zhu, S. Mak, C. Rudin, and Y. Jiang. “An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 4089–99, 2023. https://doi.org/10.1145/3580305.3599772.
Hahn S, Zhu R, Mak S, Rudin C, Jiang Y. An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 4089–99.
Hahn, S., et al. “An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2023, pp. 4089–99. Scopus, doi:10.1145/3580305.3599772.
Hahn S, Zhu R, Mak S, Rudin C, Jiang Y. An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 4089–4099.

Published In

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

DOI

ISBN

9798400701030

Publication Date

August 6, 2023

Start / End Page

4089 / 4099