Procedurally generated AI compound media for expanding audial creations, broadening immersion and perception experience


  • Grzegorz Samson The Feliks Nowowiejski Academy of Music, Bydgoszcz


Recently, the world has been gaining vastly increasing access to more and more advanced artificial intelligence tools. This phenomenon does not bypass the world of sound and visual art, and both of these worlds can benefit in ways yet unexplored, drawing them closer to one another. Recent breakthroughs open possibilities to utilize AI driven tools for creating generative art and using it as a compound of other multimedia. The aim of this paper is to present an original concept of using AI to create a visual compound material to existing audio source. This is a way of broadening accessibility thus appealing to different human senses using source media, expanding its initial form. This research utilizes a novel method of enhancing fundamental material consisting of text audio or text source (script) and sound layer (audio play) by adding an extra layer of multimedia experience – a visual one, generated procedurally. A set of images generated by AI tools, creating a story-telling animation as a new way to immerse into the experience of sound perception and focus on the initial audial material. The main idea of the paper consists of creating a pipeline, form of a blueprint for the process of procedural image generation based on the source context (audial or textual) transformed into text prompts and providing tools
to automate it by programming a set of code instructions. This process allows creation of coherent and cohesive (to a certain extent) visual cues accompanying audial experience levering it to multimodal piece of art. Using nowadays technologies, creators can enhance audial forms procedurally, providing them with visual context. The paper refers to current possibilities, use cases, limitations and biases giving presented tools and solutions.


S. Bubeck, V. Chandrasekaran, R. Eldan, J. A. Gehrke, E. Horvitz,

E. Kamar, P. Lee, Y. Lee, Y.-F. Li, S. M. Lundberg, H. Nori, H. Palangi,

M. T. Ribeiro, and Y. Zhang, “Sparks of artificial general intelligence:

Early experiments with gpt-4,”, 2023.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “Highresolution

image synthesis with latent diffusion models,” Computer

Vision and Pattern Recognition, 2021.

C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton,

S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes,

T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi, “Photorealistic textto-

image diffusion models with deep language understanding,” Neural

Information Processing Systems, 2022.

C. Gao, J. J. Green, X. Yang, S. Oh, J. Kim, and S. V. Shinkareva,

“Audiovisual integration in the human brain: a coordinate-based

meta-analysis,” Cerebral Cortex, vol. 33, no. 9, pp. 5574–5584, 11

[Online]. Available:

H. Lima, B. LimaHugo, C. G. R. dos Santos, S. G. R. Dos, S. MeiguinsBianchi,

and B. S. Meiguins, “A survey of music visualization

techniques,” ACM Computing Surveys, 2021.

M. Tiihonen, E. Brattico, J. Maksimainen, J. Maksimainen, J. Wikgren,

and S. Saarikallio, “Constituents of music and visual-art related pleasure

– a critical integrative literature review,” Frontiers in Psychology, 2017.

M. Mller, Fundamentals of Music Processing: Audio, Analysis, Algorithms,

Applications, 1st ed. Springer Publishing Company, Incorporated,

S. Latif, H. Cuay´ahuitl, F. Pervez, F. Shamshad, H. S. Ali, and

E. Cambria, “A survey on deep reinforcement learning for audiobased

applications,” Artificial Intelligence Review, vol. 56, no. 3,

pp. 2193–2240, 2023. [Online]. Available:


W. S. Peebles and S. Xie, “Scalable diffusion models with transformers,”, 2022.

S. Wu, T. Wu, F. Lin, S. Tian, and G. Guo, “Fully transformer

networks for semantic image segmentation,”, 2021. [Online].


L. Yang, Z. Zhang, and S. Hong, “Diffusion models: A comprehensive

survey of methods and applications,”, 2022.

A. Ulhaq, N. Akhtar, and G. Pogrebna, “Efficient diffusion models for

vision: A survey,” Cornell University - arXiv, 2022.

X. Pan, P. Qin, Y. Li, H. Xue, and W. Chen, “Synthesizing coherent

story with auto-regressive latent diffusion models,”, 2022.

J. Zakraoui, M. Saleh, S. Al-M´aadeed, and J. M. Alja’am, “A pipeline

for story visualization from natural language,” Applied Sciences, 2023.

H. Chen, R. Han, T.-L. Wu, H. Nakayama, and N. Peng, “Charactercentric

story visualization via visual planning and token alignment,”

Cornell University - arXiv, 2022.

Y. Z. Song, Y.-Z. Song, Y.-Z. Song, Z. R. Tam, Z. R. Tam, H.-J.

Chen, H.-J. Chen, H.-H. Lu, H.-H. Shuai, and H.-H. Shuai, “Characterpreserving

coherent story visualization,” European Conference on Computer

Vision, 2020.

S. Chen, B. Liu, B. Liu, B. Liu, B. Liu, B. Liu, J. Fu, R. Song, Q. Jin,

P. Lin, P. Lin, X. Qi, C. Wang, and J. Zhou, “Neural storyboard artist:

Visualizing stories with coherent image sequences,” arXiv: Artificial

Intelligence, 2019.

A. Maharana, D. Hannan, and M. Bansal, “Storydall-e: Adapting pretrained

text-to-image transformers for story continuation,” European

Conference on Computer Vision, 2022.

P. Dhariwal and A. Nichol, “Diffusion models beat gans on image

synthesis,” Neural Information Processing Systems, 2021.

J. Zhou, X. Shen, J. Wang, J. Zhang, W. Sun, J. Zhang, S. Birchfield,

D. Guo, L. Kong, M. Wang, and Y. Zhong, “Audio-visual segmentation

with semantics,”, 2023.

G. Irie, M. Ostrek, H. Wang, H. Kameoka, A. Kimura, T. Kawanishi,

and K. Kashino, “Seeing through sounds: Predicting visual semantic segmentation

results from multichannel audio signals,” IEEE International

Conference on Acoustics, Speech, and Signal Processing, 2019.

C. Liu, P. Li, X. Qi, H. Zhang, L. Li, D. Wang, and X. Yu, “Audio-visual

segmentation by exploring cross-modal mutual semantics,” null, 2023.

G. Yariv, I. Gat, L. Wolf, Y. Adi, and I. Schwartz, “Audiotoken:

Adaptation of text-conditioned diffusion models for audio-to-image

generation,”, 2023.

W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang,

J. Zhang, Z. Dong, Y. Du, C. Yang, Y. Chen, Z. Chen, J. Jiang, R. Ren,

Y. Li, X. Tang, Z. Liu, P. Liu, J. Nie, and J. rong Wen, “A survey of

large language models,”, 2023.

T. G¨orne, “The emotional impact of sound: A short theory of film sound

design,” null, 2019.

J. Z. Wang, S. Zhao, C. Wu, R. B. Adams, M. Newman, T. Shafir,

and R. Tsachor, “Unlocking the emotional world of visual media: An

overview of the science, research, and impact of understanding emotion,”

Proceedings of the IEEE, 2023.

X. Wang, X. Li, Z. Yin, Y. Wu, L. J. D. O. P. L. O. Brain, Intelligence,

T. University, D. Psychology, and R. University, “Emotional intelligence

of large language models,”, 2023.

S. C. Patel and J. Fan, “Identification and description of emotions by

current large language models,” bioRxiv, 2023.

Z. Akhtar and T. H. Falk, “Audio-visual multimedia quality assessment:

A comprehensive survey,” IEEE Access, 2017.

A. Mehrish, N. Majumder, R. Bharadwaj, R. Mihalcea, and S. Poria,

“A review of deep learning techniques for speech processing,”

Information Fusion, vol. 99, p. 101869, 2023. [Online]. Available:

J. Li, X. Zhang, X. Zhang, X. Zhang, X. Zhang, X. Zhang, C. Jia, J. Xu,

X. Jizheng, L. Zhang, L. Zhang, L. Zhang, Z. Li, L. Zhang, Y. Wang,

Y. Wang, W. Yue, Y. Wang, S. Ma, W. Gao, and W. Gao, “Direct speechto-

image translation,” arXiv: Multimedia, 2020.

G. Samson, “Multimodal media generation: Exploring pipeline

of procedural visual context-dependent media layer creation,”

Warsaw, p. 67, 2023, thesis (Engineering) - Polish-Japanese

Academy of Information Technology, 2023. [Online]. Available:

J. Edwards, A. Perrone, and P. R. Doyle, “Transparency in language

generation: Levels of automation,” CIU, 2020. [Online]. Available:

R. Adaval, G. Saluja, and Y. Jiang, “Seeing and thinking in pictures: A

review of visual information processing,” Consumer Psychology Review,

P. Gholami and R. Xiao, “Diffusion brush: A latent diffusion modelbased

editing tool for ai-generated images,”, 2023.

P. Li, Q. Huang, Y. Ding, and Z. Li, “Layerdiffusion: Layered controlled

image editing with diffusion models,”, 2023.

X. Zhang, W. Zhao, X. Lu, and J. Chien, “Text2layer: Layered image

generation using latent diffusion model,”, 2023.

X. Ma, Y. Zhou, X. Xu, B. Sun, V. Filev, N. Orlov, Y. Fu, and H. Shi,

“Towards layer-wise image vectorization,” Computer Vision and Pattern

Recognition, 2022.

M. Dorkenwald, T. Milbich, A. Blattmann, R. Rombach, K. Derpanis,

and B. Ommer, “Stochastic image-to-video synthesis using cinns,”

Computer Vision and Pattern Recognition, 2021.

Y. Hu, C. Luo, and Z. Chen, “Make it move: Controllable image-tovideo

generation with text descriptions,” Computer Vision and Pattern

Recognition, 2021.

M. Stypulkowski, K. Vougioukas, S. He, M. Ziba, S. Petridis, and

M. Pantic, “Diffused heads: Diffusion models beat gans on talking-face

generation,”, 2023.

L. Shen, X. Li, H. Sun, J. Peng, K. Xian, Z. Cao, and G.-S. Lin, “Makeit-

d: Synthesizing a consistent long-term dynamic scene video from a

single image,”, 2023.

J. Wu, J. J. Y. Chung, and E. Adar, “Viz2viz: Prompt-driven stylized

visualization generation using a diffusion model,”, 2023.

C. K. Praveen and K. Srinivasan, “Psychological impact and influence

of animation on viewer’s visual attention and cognition: A systematic

literature review, open challenges, and future research directions.” Computational

and Mathematical Methods in Medicine, 2022.

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