Composing jazz music pieces using LSTM neural networks approach

Xincheng Zhang

doi:10.54254/2755-2721/68/20241414

Research Article

Open access

Published on 6 June 2024

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Zhang,X. (2024). Composing jazz music pieces using LSTM neural networks approach. Applied and Computational Engineering,68,128-136.

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Composing jazz music pieces using LSTM neural networks approach

Xincheng Zhang *^,1,

¹ School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/68/20241414

Abstract

The domain of artificial intelligence has increasingly extended into the creative arts, aiming to emulate and augment human creativity with automated processes. This is particularly evident in the field of music, where AI's ability to learn and produce intricate compositions has attracted significant attention. This study explores the challenge of generating jazz music using artificial intelligence, specifically focusing on the application of Long Short-Term Memory (LSTM) neural networks for jazz composition. By optimizing the model to address the genre's complexity, the research demonstrates the LSTM's capacity to capture and reproduce jazz's essential harmonic progressions and rhythmic nuances. Quantitative analyses show high accuracy and a deep understanding of musical structures, whereas qualitative feedback confirms the model's efficacy in producing compositions that embody jazz's spontaneity. Despite its achievements, the model's tendency to generate repetitive sequences suggests areas for improvement. This paper advances the field of AI in music, illustrating the potential of LSTM networks to mimic complex musical genres and emphasizing the necessity of ongoing model refinement to foster creativity. It highlights the evolving role of machine learning in music generation, proposing a foundation for future work aimed at diminishing the gap between AI capabilities and artistic expression.

Keywords

LSTM networks, jazz music, MIDI, model refinement

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Cite this article

Zhang,X. (2024). Composing jazz music pieces using LSTM neural networks approach. Applied and Computational Engineering,68,128-136.

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 6th International Conference on Computing and Data Science

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

ISBN：978-1-83558-457-6(Print) / 978-1-83558-458-3(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.68

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

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