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Computational Creativity Models for Automated Multimedia Content Generation

Gokhale Ranade

Bharati Vidyapeeth's College of Engineering, Shivaji University, Kolhapur, India

Abstract

Computational creativity refers to the study and development of algorithms, systems, and models that can exhibit creativity that resembles or complements human creative processes. With the exponential growth of digital content and multimedia data, automated multimedia content generation has become a pivotal research area in artificial intelligence (AI) and machine learning (ML). This paper investigates computational creativity models specifically in the context of generating multimedia content such as images, videos, music, and interactive narratives. We explore foundational theories of creativity, the adaptation of generative models—such as generative adversarial networks (GANs), variational autoencoders (VAEs), transformer architectures, and evolutionary algorithms—and their applications in multimedia creativity tasks. Through an extensive literature review, we examine developments from early rule-based and knowledge-based systems to state-of-the-art deep learning techniques. The research methodology outlines a framework for evaluating these models based on creativity metrics, quality assessments, and user perceptions. The study also discusses advantages and disadvantages, highlighting scalability, novelty, coherence, and ethical considerations. Results illustrate both the potential and limitations of current models, while conclusions and future work propose avenues for enhancing automated creativity in multimedia generation.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 6 No. 1 (2023): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2023.0601001

Pages

9201-9209

Published

January 11, 2023
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Gokhale Ranade (%2023). Computational Creativity Models for Automated Multimedia Content Generation. International Journal of Science, Research and Technology , Vol. 6 No. 1 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 9201-9209. https://doi.org/10.15662/IJSRAT.2023.0601001

References

1. Boden, M. A. (1998). Creativity and artificial intelligence. Artificial Intelligence, 103(1–2), 347–356.
2. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems.
3. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114.
4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
5. Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.
6. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. MIT Press.
7. Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. MIT Press.
8. Meehan, J. R. (1976). Tale-Spin, an interactive program that writes stories. Proceedings of the 5th International Joint Conference on Artificial Intelligence.
9. Wiggins, G. A. (2006). A preliminary framework for description, analysis and comparison of creative systems. Journal of Artificial Intelligence Research, 30, 379–410.
10. Colton, S. (2008). Creativity vs. the perception of creativity in computational systems. AAAI Spring Symposium.
11. Boden, M. A. (2009). Creative minds and computers. Psychology Press.
12. McCormack, J., Gifford, T., & Hutchings, P. (2019). Design principles for creative AI interface design. Journal of Creative AI.
13. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.
14. Machado, P., & Cardoso, A. (1998). All the truth about NEUROGEN. In Proceedings of the International Computer Music Conference.
15. Hertzmann, A. (2018). Can computers create art?. Arts, 7(2), 18.
16. Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating "art" by learning about styles and deviating from style norms. ICCC.
17. McDermott, J. (1996). The science of qualitative reasoning. MIT Press.
18. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature.
19. Simonton, D. K. (1999). Origins of genius: Darwinian perspectives on creativity. Oxford University Press.
20. Boden, M. A., & Edmonds, E. A. (2009). Tutorial on creative systems. Journal of Knowledge-Based Systems.
21. Reinsel, D., Gantz, J., & Rydning, J. (2018). The digital universe in 2025: Will there be enough storage?. International Data Corporation.