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A Novel Probabilistic Diffusion Model Based on the Weak Selection Mimicry Theory for the Generation of Hypnotic Songs

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  • Wenkai Huang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Feng Zhan

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

The constraints in traditional music style transfer algorithms are difficult to control, thereby making it challenging to balance the diversity and quality of the generated music. This paper proposes a novel weak selection-based music generation algorithm that aims to enhance both the quality and the diversity of conditionally generated traditional diffusion model audio, and the proposed algorithm is applied to generate natural sleep music. In the inference generation process of natural sleep music, the evolutionary state is determined by evaluating the evolutionary factors in each iteration, while limiting the potential range of evolutionary rates of weak selection-based traits to increase the diversity of sleep music. Subjective and objective evaluation results reveal that the natural sleep music generated by the proposed algorithm has a more significant hypnotic effect than general sleep music and conforms to the rules of human hypnosis physiological characteristics.

Suggested Citation

  • Wenkai Huang & Feng Zhan, 2023. "A Novel Probabilistic Diffusion Model Based on the Weak Selection Mimicry Theory for the Generation of Hypnotic Songs," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3345-:d:1206768
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    References listed on IDEAS

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    4. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    5. Thomas N. Sherratt, 2002. "The evolution of imperfect mimicry," Behavioral Ecology, International Society for Behavioral Ecology, vol. 13(6), pages 821-826, November.
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