Investigating MIDI data simplification by AI models

Braden Ou

doi:10.54254/2755-2721/21/20231129

Research Article

Open access

Published on 23 October 2023

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Ou,B. (2023). Investigating MIDI data simplification by AI models. Applied and Computational Engineering,21,114-120.

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Investigating MIDI data simplification by AI models

Braden Ou *^,1,

¹ The Quarry Lane School, Dublin, CA 94568, the U.S.

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/21/20231129

Abstract

According to the Smithsonian Institution, the art of making music has existed for over 35,000 years. As musical technology has improved, the music of the time has also improved and adapted to the new technology. In the recent expansion of technology from generative AI, text and image generation have become not only possible but also competitive with human-created text and images. As such, the development of AI-generated music is increasingly sparking considerable interest among musicians and developers alike, raising questions about the potential of AI to enhance or even replace human musical creativity. This paper will first explore the advancements of AI-generated music. Next, it will delve into the technologies and methodologies involved in generating music, as well as its current limitations using a basic LSTM (Long Short-Term Memory) model. Finally, it will explore the implications of this music for the whole music industry. By examining these various facets of AI-generated music, this research provides insights into AI's potential role in shaping the future of music. According to the analysis, a rudimentary AI model trained on complex music can produce music that is fairly elementary. Overall, these results shed light on guiding further exploration of the interaction between artificial intelligence and music.

Keywords

artificial intelligence, machine learning, music

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References

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Cite this article

Ou,B. (2023). Investigating MIDI data simplification by AI models. Applied and Computational Engineering,21,114-120.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-033-2(Print) / 978-1-83558-034-9(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Alan Wang, Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.21

ISSN：2755-2721(Print) / 2755-273X(Online)

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